PART B · 2016-05-09 · Cooperation 600932 Collaborative project (IP) FP7-ICT-2011-9 (Information and Communication Technologies) MD-PAEDIGREE 600932 MD PAEDIGREE - Workplan Vs 3

Cooperation

600932

Collaborative project (IP)

FP7-ICT-2011-9 (Information and Communication Technologies)

MD-PAEDIGREE

600932 MD PAEDIGREE - Workplan Vs 3 -– April 28May 9thth, 2016

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PART B COLLABORATIVE

PROJECT

TABLE OF CONTENTS

B.1 Concept and objectives, progress beyond the state‐of‐the‐art, S/T methodology and work

plan ............................................................................................................................................. 4

B.1.1 Concept and objectives ....................................................................................................... 4

B.1.1.1 Disease Modelling ‐ Clinical background and scientific basis .................................................................. 6 B.1.1.1.1 Cardiomyopathy ................................................................................................................................... 6 B.1.1.1.2 Risk of cardiovascular disease in obese children and adolescents ...................................................... 7 B.1.1.1.3 Juvenile Idiopathic Arthritis (JIA)......................................................................................................... 7 B.1.1.1.4 Neurological and Neuro‐muscular Diseases (NND) ........................................................................... 8

B.1.2 Progress beyond the State‐of‐the‐Art ................................................................................ 9

B.1.2.1 Disease modelling ........................................................................................................................................ 9 B.1.2.1.1 Modelling and simulation for cardiomyopathies .............................................................................. 9 B.1.2.1.2 Cardiovascular disease risk in obese children and adolescents ...................................................... 12 B.1.2.1.3 JIA ....................................................................................................................................................... 14 B.1.2.1.4 NND: protocols and personalised models in Advanced Clinical Gait Analysis ................................ 17

B.1.2.2 Description of MD‐Paedigree’s modelling and data baseline ................................................................ 20 B.1.2.2.1 Baseline in terms of re‐use of models already available in the consortium.................................. 20 B.1.2.2.2 Baseline in terms of data already acquired to the consortium, and additional data collection . ..21

B.1.2.3 The MD‐Paedigree VPH Infostructure and Digital Repository ............................................................... 22 B.1.2.3.1 Service‐oriented knowledge utility (SOKU) ...................................................................................... 22 B.1.2.3.2 Data access and query formulation .................................................................................................. 22 B.1.2.3.3 Distributed processing and GPU support ......................................................................................... 23 B.1.2.3.4 Intelligent mining, modelling, reasoning and simulation framework ............................................. 23 B.1.2.3.5 Holistic model‐guided personalised medicine ................................................................................. 24 B.1.2.3.6 Compliance with guidelines for model based‐drug development (MBDD) .................................... 24

B.1.2.4 D‐Paedigree’s Infostructure Baseline ....................................................................................................... 25

B.1.3 S/T Methodology and Associated Work Plan .................................................................. 27

B.1.3.1 Overall strategy and general description ................................................................................................. 27

B.1.3.2 Components and their Interdependencies .............................................................................................. 28

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B.1.3.3 D‐Paedigree’s main objectives and related milestones ........................................................................ 28

1. Patient‐specific computer‐based predictive models of cardiomyopathies ..................................................29

2. Patient‐specific computer‐based predictive models of cardiovascular disease risk in obese children and adolescence .......................................................................................................................................................... 29

3. Patient‐specific computer‐based predictive models of juvenile idiopathic arthritis ...................................29

4. Patient‐specific computer‐based predictive models of NND ..........................................................................29

5. Newly‐defined workflows for personalised predictive medicine at the point of care ................................30

B.1.3.4 Timing of work packages and their components ................................................................................... 30

B.1.3.5 Performance/research indicators ............................................................................................................ 33

B.1.3.6 Risk Analysis & Mitigation Plan ............................................................................................................... 33

Risk .............................................................................................................................................................................. 33

Management Risk .................................................................................................................................................... .35

Technical Risk ........................................................................................................................................................... .36

B.2 Implementation ...................................................................................................................................... 37

B.2.1 Management Structure and Procedures ........................................................................ 37

B.2.2 Beneficiaries ................................................................................................................... 42

B.2.3 Consortium as a whole ................................................................................................... 66

B.2.3.1 Brief description of technical partners’ skills and expected cooperation between them for each specific action .......................................................................................................................................... 67

B.2.3.2 Subcontract ................................................................................................................................................ 68

B.2.3.3 Third parties .............................................................................................................................................. 68

B.2.4 Resources to be committed............................................................................................ 68

B.2.4.1 Use of resources ........................................................................................................................................ 68 B.2.4.1.1 Distribution of resources by activity ................................................................................................ 68 B.2.4.1.2 Distribution of resources by cost item ............................................................................................. 69 B.2.4.1.3 Use of resources for each partner ................................................................................................... 70

B.3 Impact ...................................................................................................................................................... 93

B.3.1 Strategic impact .............................................................................................................. 93

B.3.1.1 D‐Paedigree’s impact on listed targets ......................................................................................................... 93 B.3.1.1.1 More predictive, individualised, effective and safer healthcare .................................................... 93 B.3.1.1.2 Reinforced leadership of European industry and strengthened multidisciplinary research

excellence in supporting innovative medical care ........................................................................ 94 B.3.1.1.3 Improved interoperability of biomedical information and knowledge ............................................ 94 B.3.1.1.4 Increased acceptance and use of realistic and validated models that allow researchers from

different disciplines to exploit, share resources and develop new knowledge .......................... 95

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B.3.1.1.5 Accessibility to existing knowledge by bio‐medical researchers through the VPH repositories linking data with models will prove the large‐scale benefits of having both the data and models readily available ............................................................................................................... .96

B.3.1.1.6 Steps needed to realise impact ...................................................................................................... .96 B.3.1.1.7 Need for a European rather than a national approach ................................................................ .96 B.3.1.1.8 Relation to other national or international research activities .................................................... .96

B.3.1.2 Applications scenarios ............................................................................................................................ .97 B.3.1.2.1 Cardiomyopathies ........................................................................................................................... 97 B.3.1.2.2 CVD risk in obese children .............................................................................................................. .98 B.3.1.2.3 Juvenile Idiopathic Arthritis ............................................................................................................ .98 B.3.1.2.4 Neurological and neuromuscular diseases. ....................................................................................... .99

B.3.1.3 Societal impact of applying VPH to paediatrics: the crucial role of outcomes analysis ................... 100

B.3.2 Plan for the use and dissemination of foreground ......................................................... 101

B.3.2.1 Dissemination & Training ....................................................................................................................... 101

B.3.2.2 Exploitation.............................................................................................................................................. 104

B.3.2.3 Management of Intellectual Property ................................................................................................... 105

B.4 Ethical issues .............................................................................................................................. 105

B.4.1 Patient enrollment and use of new data ........................................................................ 106

B.4.2 Use of existing data ......................................................................................................... 107

Appendix

References. .................................................................................................................... 110

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B.1 Concept and objectives, progress beyond the state-of-the-art, S/t methodology and work plan

B.1.1 Concept and objectives

A 2009 McKinsey study titled When clinicians lead argued that “health-care systems that are serious about transforming themselves must harness the energies of their clinicians as organizational leaders”. MD-Paedigree moves from this assumption and represents a clinically-driven and strongly VPH-rooted project, where 7 world-renowned clinical centres of excellence pursue improved interoperability of paediatric biomedical information, data and knowledge by developing together a set of reusable and adaptable multi-scale models for more predictive, individualised, effective and safer paediatric healthcare, being scientifically and technologically supported by one of the leading industrial actors in medical applications in Europe operating in conjunction with highly qualified SMEs and some of the most experienced research partners in the VPH community.

MD-Paedigree validates and brings to maturity patient-specific computer-based predictive models of various paediatric diseases, thus increasing their potential acceptance in the clinical and biomedical research environment by making them readily available not only in the form of sustainable models and simulations, but also as newly-defined workflows for personalised predictive medicine at the point of care. These tools can be accessed and used through an innovative model-driven infostructure powered by an established digital repository solution able to integrate multimodal health data, entirely focused on paediatrics and conceived of as a specific implementation of the VPH-Share project, planned to be fully interoperable with it and cooperating, through it, also with p-Medicine.

In MD-Paedigree, the VPH Infostructure is designed to accommodate the chosen paediatric clinical areas, starting from the considerable experience capitalized in the Health-e-Child and Sim-e-Child projects. The latter developed grid and cloud-based eHealth repositories, models and simulations for specific diseases, and, particularly building on top of current developments within OPBG (Ospedale Pediatrico Bambino Gesù), further eHealth tools for data management and distributed high-performance computing which aim at gradually transferring into clinical practice the most advanced modelling in paediatric cardiology to support more precise outcomes analysis of pathologies and develop optimal therapies.

MD-Paedigree aims at achieving high-level semantic interoperability, thus requiring standards enabling the clinical contents to be interpreted consistently across the different EHR regimes, while complete clinical interoperability between systems will require widespread and dependable access to maintained collections of coherent and quality-assured semantic resources, including models that provide clinical context, mapped to interoperability standards for EHR and PHR and biomedical data, linked to well specified terminology value sets, derived from high quality ontologies.

In order to achieve semantic support at this level, MD-Paedigree takes advantage of recent work achieved in other EC semantic health and Ontology Based Data Access (OBDA) related projects such as SemanticHealthNet and DebugIT. MD-Paedigree also intends to comply with terminological and data interchange standards currently being developed within epSOS, in particular for the Patient Summary. As for biological data, MD-Paedigree will rely on OBO Foundry resources and BioDBcore recommendations [P. Gaudet et al., 2010]. In addition, it aims to relate research, publications, experiments, and data joining forces with other EC – open access related – projects like OpenAIRE, OpenAIREplus.

MD-Paedigree’s goals therefore are to:

integrate and share highly heterogeneous biomedical information, data and knowledge, using best practices from the biomedical semantic Web,

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develop holistic search strategies to seamlessly navigate through and manage the integrative model-driven infostructure and digital repository,

jointly develop reusable, adaptable and composable multi-scale VPH workflow models,

support evidence-based translational medicine at the point of care, and

ultimately facilitate collaborations within the VPH community.

MD-Paedigree elaborates on a decade of developments initially pioneered in the European FP5 MammoGrid and FP6 Health-e-Child projects, which were then brought further in FP7 Sim-e-Child. More particularly, it leverages on the grid Gateway concept, allowing scientists to abstract from the complexity of underlying grids, clouds and other computing resources they need to use. Nowadays, Science Gateways represent an important emerging paradigm for providing integrated infrastructures. According to Wilkins a Science Gateway is “a community-developed set of tools, applications, and data that are integrated via a portal or a suite of applications, usually in a graphical user interface, that is further customised to meet the needs of a specific community. Gateways enable entire communities of users associated with a common discipline to use national resources through a common interface that is configured for optimal use. Researchers can focus on their scientific goals and less on assembling the cyberinfrastructure they require. Gateways can also foster collaborations and the exchange of ideas among researchers”. MD-Paedigree thus intends to reuse the latest Service Oriented Architecture (SOA) based Gateway released in Sim-e-Child, which enables secure and reliable access to abstracts from and integrates all forms of applications and data useful to users. The Gateway materializes as a layered architecture of standard secure (generic medical) services running on top of a grid infrastructure, which is physically installed at the participating clinical centres. Thanks to these on-site access points, users can transparently utilize a number of heterogeneous computing resources, ranging from local databases, to the distributed grid infrastructure regardless of their location and available connectivity. The Gateway supports the major principles of an SOA and exposes a significant set of biomedical utilities to date.

MD-Paedigree will extend the Gateway and demonstrate a reasonably well-scoped use-case of the Service Oriented Knowledge Utility (SOKU) vision, as published by the European Commission in the Future for European Grids: Grids and Service Oriented Knowledge Utilities report, to address the challenge of delivering personalised care to patients.

MD-Paedigree will implement the SOKU vision, to facilitate the design and development of innovative predictive models as reusable and adaptable workflows of data mining applications and turning the latter into clinically validated decision support tools, made available at the point of care. This is what illustrates where reusable VPH models (in the centre) are incubated in the system with progressive semantic enrichment and model transformations in a cycle (the yellow spiral) witnessing the intervention of both automated database-guided learning and data integration and knowledge experts validation. As these models become more mature, they are then clinically validated by participating centres and concerned clinical researchers, and ultimately made available at the point of care thanks to the physical distribution and computational nature of the MD-Paedigree model-driven infostructure.

Taking its roots from a well-established distributed digital repository, the MD-Paedigree VPH infostructure will thus hatch in a plethora of breakthrough decision support applications, as is illustrated with the petals of the SOKU flower (top-left of Figure 1).

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Figure 1

MD-Paedigree as a Service Oriented Knowledge Utility (SOKU)

B.1.1.1 Disease Modelling - Clinical Background and Scientific Basis

The following paragraphs present a short clinical background on the selected pathologies that will be modeled in the project, including the current challenges and uncertainties in the management of these diseases.

B.1.1.1.1 Cardiomyopathy

In paediatric cardiovascular disease, predicting how patients will respond to treatments (operations, catheter interventions, pharmacology), which treatments to use, and when to treat can be difficult to define due to small patient numbers and limited outcome data. When children present with new onset heart failure, there are five possible outcomes: full recovery, dilated cardiomyopathy (DCM) requiring drug therapy, DCM requiring transplantation or mechanical support, another diagnosis (other forms of cardiomyopathy, metabolic disease) or death. At presentation, however, it is very difficult to predict which group any patient will end up in. Data suggests that good systolic function and younger age are good prognostic indicators for survival, but better prognosticators are necessary.

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Over the last decade, there has been a huge investment into information technology and computer modelling to build models of the heart that are able to gather any kind of clinical information and produce realistic representations of the cardiovascular system. Modelling of patient bioinformatic data may provide better insight into prognosis of cardiomyopathies, which would help in patient management and in telling families how their child will progress. Would he/she recover completely or would he/she require heart transplant? These models have now reached high levels of reproducibility, opening new avenues for more efficient, safer, and cost-effective patient management. However, their comprehensive validation is still limited.

MD-Paedigree will re-use the models developed in Health-e-Child and Sim-e-Child and extend them to cardiomyopathies. The objective is to capture the main features of the cardiovascular system, including the heart, arteries and peripheral circulation, to predict cardiomyopathy progression and plan therapies like heart transplant and ventricular assist devices. Investigative data provided by imaging, pressure monitoring, clinical observations and exercise will be used to build these models and to validate them, by comparing model prediction with actual outcome. By merging all scattered information obtained from different diagnostic tools in clinical practice, and obtaining a generative model of heart function in children, our model will provide cardiologists the tools to deliver patients the best possible medical care.

B.1.1.1.2 Risk of Cardiovascular Disease in Obese Children and Adolescents

The World Health Report 2002 revealed that, in developed countries, approximately one third of all coronary heart diseases and ischaemic strokes and almost 60% of hypertensive diseases can be directly attributed to obesity. These figures confirm obesity as one of the primary risk factors for cardiovascular disease (CVD), a risk factor that originates early in life. As autopsy studies have shown, the levels of lipids, blood pressure, and obesity in the young are directly associated with the extent of early atherosclerosis of the aorta and coronary arteries. For this reason, it is of particular concern that there has been a significant increase in childhood and adolescent obesity over the last decade. In the United States, 32% of children and adolescents are now at or above the eighty-fifth percentile of the 2000 BMI-for-age growth charts but also in the United Kingdom, the prevalence of obesity in children is approaching one third.

One of the challenges concerning the study of childhood obesity and its influence on CVD risk is the required time span for longitudinal studies: cardiovascular events occur mostly later in adulthood, which means that longitudinal studies have to comprise several decades. Nonetheless, cross-sectional studies are able to show correlation between childhood obesity and established surrogate markers for CVD, such as atherosclerosis and cardiac hypertrophy. The Strong Heart Study, which analysed data from over 450 adolescents, demonstrated that in patients with obesity and/or metabolic syndrome a significantly higher prevalence of left ventricular hypertrophy and left atrial dilation paired with impairment in both systolic and diastolic function is observed.

Insulin resistance (IR) is an established determinant in the pathogenesis of CVD; it is constantly observed in patients with hypertension, dyslipidemia and atherosclerosis. Evidence supports firmly that body fat distribution (subcutaneous, visceral, muscle and hepatic fat) modulates IR and cardiovascular risk more than total body adiposity, thus explaining why some individuals who are seemingly equally obese and share common lifestyle and dietary habits tend to have higher IR and CVD risk than others.

MD-Paedigree will integrate the variety of known biomarkers for CVD risk assessment into one common framework, enhance body fat distribution biomarker measurement, and analyse interdependencies between the biomarkers. In addition, MD-Paedigree will develop computational models with high predictive power to better understand the mechanism of CVD development. These models will also allow the simulation of interventions to make personalised predictions for the optimal therapy.

B.1.1.1.3 Juvenile Idiopathic Arthritis (JIA)

Juvenile idiopathic arthritis (JIA) is a broad term that describes a clinically heterogeneous group of arthritis which has an onset before age of 16 years, lasts more than 6 weeks and is of unknown origin.

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The cause and pathogenesis of JIA are still poorly understood, but likely they include both genetic and environmental components. Moreover, disease heterogeneity implies that different factors probably contribute to its pathogenesis and causes. Affected joints develop synovial proliferation and infiltration by inflammatory cells which may ultimately lead to destructive lesions of joint structures, disability and high disease-related costs. Indeed, JIA which affects approximately one in 1,000 children represents the leading cause of childhood disability from a musculoskeletal disorder.

Current classification, which is based on clinical criteria, is still unsatisfactory: considerable heterogeneity in disease course and treatment response exists, both between and within subtypes of JIA. Unfortunately, the present ability to predict the disease course and outcome is limited. Within the FP6 Health-e-Child project, ICT tools for diagnosis and scoring of JIA, based on image data of the wrist, have been developed. This framework is the basis for the developments planned for MD-Paedigree.

Comprehensive and accurate computer models derived from patient-specific data across multiple scales covering body, organs, tissues, and molecular levels are developed. This data is gathered and stored in a standardised manner building upon the Health-e-Child software tools developed for wrist analysis in the context of JIA. These tools are extended for the purpose of integrating model information related to a wider range of joints, covering morphology, gait analysis, bio/genetic data.

The tools to be developed will also include the aspect of a multidimensional longitudinal analysis that yields the opportunity to identify potential new outcome measures (imaging or biological biomarkers) for the assessment of treatment efficacy. Furthermore, the prognostic value on an individual level of multidimensional data, including modern imaging modalities, genetic and meta-genetic data will be explored through the development and integration of appropriate data clustering methods. By collecting patient specific multi-scale and multi-dimensional information and automating image and data analysis at the point of care, this project has a strong clinical impact on early diagnosis, prediction of disease and of treatment outcome.

B.1.1.1.4 Neurological and Neuro-muscular Diseases (NND)

In Neurological and Neuromuscular Diseases (NND) as well as in certain chronic paediatric diseases of the musculoskeletal system, treatments are strongly guided by maximising the walking function of the human movement system, because walking is considered as clinically meaningful by patients. This generalises to most mobility-related functions. The most common paediatric disorder within the NND disease area is Cerebral Palsy (CP) whose incidence ranges between 2 to 3.6 per 1,000 live births. CP includes a group of non-progressive, often changing, motor impairment syndromes, secondary to lesions in the sensory-motor cortex and corticospinal tract, arising in the early stages of the child’s development. Conventional clinical gait analysis (CGA) is already an important tool in the treatment of children with CP that aims to improve or sustain walking performance, but its potential is under-utilised and recent developments need full exploration. The second important disorder is Charcot Marie Tooth disease (CMT), one of the most common inherited neurological disorders, affecting approximately 1 in 2,500.The neuropathy of CMT affects both motor and sensory nerves. A typical feature includes weakness of the foot and lower leg muscles, which may result in foot drop and a high-stepped gait with frequent tripping or falls. Foot deformities, such as high arches and hammertoes (a condition in which the middle joint of a toe bends upwards) are also characteristic due to weakness of the small muscles in the feet. For ambulant CMT patients, new methods for functional motor evaluation based on gait modelling would allow to increase sensitivity to change in assessing weakness and tripping or falling.The third disorder, Duchenne Muscular Dystrophy (DMD) is the most common and severe form of muscular dystrophy, with an incidence around 1 in 3,600 juveniles. This disorder is caused by a mutation in the dystrophin gene, that codes for a protein which is a major structural component of the muscle. The absence of dystrophin results in muscle degeneration, difficulty in walking (resulting in wheelchair use from 14 years of age), followed by loss of arms and hands function. In the last few years, following a rapidly increasing number of potentially effective therapeutic approaches for DMD, the request for validated and sensitive outcome measures to be used in clinical trials has increased.

Although walking is a common task executed by a healthy individual in a seemingly effortless manner,

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it implies a complex involvement of inputs from several senses (visual, vestibular, proprioceptive, somatosensory), partly automated by the so called spinal central pattern generator (CPG). These inputs are known to interact with each other, but the way in which this is performed is not fully exploited at present. Nevertheless, the current insights are certainly at an advanced state that allows for meaningful application towards pathological walking, where decision support is needed. In the clinical practice of specialised centres, CGA is used to evaluate the joint and muscle functions in their functional context, i.e. during gait. Common CGA measures 3D kinematics (by 3D optoelectronic registration of skin mounted markers). Each relevant degree of freedom (DOF) is expressed as a function of the gait cycle. Moreover, using a mass distribution model and measuring ground reaction forces, the net moments for each DOF are calculated using inverse dynamics analysis. Muscle activation patterns, for all relevant muscles, are measured using electromyography (EMG) for each targeted muscle. Finally, the energy cost of walking can be evaluated using metabolic measurements.

CGA is a special form of personalised computer-aided medicine that supports clinical decision making. Unfortunately, the output of CGA is not yet in a format that permits clear, unambiguous interpretation, because of the redundancy of the Neuro‐Musculo‐Skeletal System (NMSS) which obstructs distinguishing cause from compensation. Even though recent developments in modelling the NMS Physiome as a part of EU funded Virtual Physiological Human efforts are at an advanced state, their results have not yet been implemented in clinical practice, and the full potential of CGA still needs to be reaped. A combination of standard protocols of gait analysis, biophysical modelling and large scale statistical analysis can therefore be expected to provide a powerful framework for meaningful interpretation.

B.1.2 Progress beyond the State-of-the-Art

B.1.2.1 Disease modelling

B.1.2.1.1 Modelling and simulation for cardiomyopathies

MD-Paedigree couples advanced image analysis techniques with computational models of cardiac function to enhance the current standard of medical care. In the following, the expected progress over the state of the art is presented for all aspects of the work.

Cardiac Anatomy

The first step of the analysis is to compute a detailed model of the cardiac anatomy of a patient. Since 2005, members of our consortium have been driving a new paradigm in medical image analysis by promoting the collection and annotation of large databases, creating the framework for the application of advanced machine learning techniques for the parsing and quantification of volumetric, time-varying data sets. New techniques such as Probabilistic Boosting Tree, Probabilistic Boosting Network, Marginal Space Learning, Shape Regression Machine, and Trajectory Spectrum Learning now permit the automatic, fast and efficient extraction of multiple anatomical structures from 4D Echo, CT and MR images.

Figure 2 Modelling multiple heart components

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In the course of the Health-e-Child and Sim-e-Child projects, we have developed modules based on these techniques to extract the anatomy and dynamics of left and right ventricle, and left and right atria, aorta, aortic and mitral valves and pulmonary valve and trunk.

Our aim for MD-Paedigree is to integrate these different modules into one robust framework to extract a dynamic anatomical model of the complete heart from MRI and echocardiography data (Figure 3). This will yield a holistic view of the cardiac system, as required by clinicians, especially in the context of cardiomyopathies and their associated complex dysfunctions.

A second aspect considered in MD-Paedigree is the analysis of myocardial fibre structure. Fibre architecture plays an important role in the realistic modelling of electrical and mechanical heart activity, but it is not yet possible to acquire in-vivo in-situ images of heart fibres in clinical

routine.

To cope with this limitation, computational models usually rely on generic fibre orientations. A common approach is to synthesise the variation of fibre orientation using rule-based methods. As a more realistic alternative, we proposed statistical models of heart fibres based on diffusion tensor images. In MD-Paedigree, we will integrate such statistical fibre models into our comprehensive anatomical model.

Electromechanical Modelling of Heart Function

Cardiac electromechanics have been investigated thoroughly in the past 50 years. A number of computational models of cardiac electrophysiology and cardiac tissue mechanics have been developed. In Health-e-Child, we employed such models to predict effects of pulmonary valve replacement in repaired tetralogy of Fallot patients (Figure 4). Application to cardiac resynchronization therapy has also been investigated. An important challenge is the personalisation of model parameters. Depending on the patient data that are available, not all parameters can always be precisely estimated, which in turn induces variability on the simulation results. In order to employ the simulation output as a computer-aided diagnosis tool, it is crucial to quantify the uncertainty on the personalised parameters and to estimate the sensibility of the prediction with respect to this uncertainty.

In MD-Paedigree, we will re-use models already available in the consortium (developed by INRIA and SCR) to simulate cardiomyopathies and therapies. In particular, aspects of the arterial circulation will be integrated as boundary conditions and modelled using quasi 1D methods with visco-elastic walls. We will also develop a framework to combine the output of these models into a consensus prediction and a variability map, framework that can be enhanced by additional models from the VPH community.

Haemodynamic Modelling of Heart Function

Figure 3 Computational model of heart fibres

Figure 4 Electromechanical simulation of tetralogy of Fallot (left panel), compared with observed motion on MRI (right panel) [Mansi et al, 2009a].

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Haemodynamics plays a significant role in heart function and in determining the progression of cardiomyopathies. With recent advances in patient-specific 4D anatomical modelling and 3D flow measurement techniques, it has become possible to employ computational fluid dynamics (CFD) for haemodynamic assessment and subsequent validation in cardio-vascular applications. Flow patterns and underlying flow parameters obtained from such simulations may be used for early diagnosis, prediction and benchmarking treatment outcomes.

While most previous approaches have focused on a single cardiac component, we have recently performed simulations of blood flow in the whole heart using high-quality patient-specific heart derived from 4D CT, as part of the Sim-e-Child project.

This was the first time that 4D physiological models of a patient’s valves together with the models of the chambers, myocardium, and main vasculature captured from 4D CT have been used to provide patient-specific constraints for the simulations of the blood flow inside the heart.

Another important aspect is the arterial circulation. In Sim-e-Child, we developed efficient numerical methods for 3D-1D and 3D-0D coupling. We successfully used these methods to couple 3D aortic CFD simulations with both 1D distal vessels and 0D micro-vessel models, and reported excellent agreement between in-vivo and simulated pressure drops across coarctations. More recently, we have also developed estimation algorithms for determining the boundary conditions from routine flow (echo Doppler) and pressure (cuff) measurements.

MD-Paedigree aims to extend our current methodology (Figure 5), which uses a robust one-way interaction to transfer momentum from the moving solid walls to the blood, to a two-way coupled framework that fully models fluid structure interaction (FSI) physics with patient-specific electromechanical models of the heart. The coupling between fluid and solid will use a previously tested robust algorithm to exchange information between the involved solvers. The blood stress tensor provides traction forces at the endocardium surface, used as boundary conditions by the electromechanical model, while the endocardium velocities are used as boundary conditions for the fluid flow computations. Our FSI model will be also coupled with the models of the systemic and pulmonary arterial circulation for a holistic view of the cardiovascular system at various states (rest, exercise, under vasodilating/vasoconstricting drugs, etc.).

While haemodynamic simulations can deliver deep insights into particular disorders, they imply significant computational costs. Even using high-performance computing and Grid technologies, the time required for one simulation does often extend the time available in clinical routine. In order to improve the clinical applicability of this technology, in MD-Paedigree we will explore atlas-based techniques of reduced models to speed up calculations. This allows an order of magnitude of reduction in the number of parameters with reasonable accuracy. Additionally, we will explore the possibility to further regress the common reduced basis not only from the flow but also from additional models or clinical variables, in order to obtain disease/patient-specific reduced flow bases.

Figure 5 One-way coupling between flow and electromechanics [Mansi

2012]

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Statistical Model of Heart Remodelling

In addition to biomechanical or haemodynamic simulations, statistical shape analysis has shown its potential to assess the severity of a disease and predict its evolution (Figure 6). Since the direct modelling of biological phenomena involved in long-term cardiac remodelling is very difficult, MD-Paedigree aims to use recent advances in deformation-based shape modelling to model the evolution of the heart over time and to potentially stratify the disease.

Thanks to an underlying 3D deformation model, such methods can seamlessly integrate not only the shape but also spatial variables such as physical and physiological parameters, flow patterns, etc. Recent advances in diffeomorphic registration have shown the feasibility of extracting a sparse multi-scale representation of deformations in registration. By regressing these sparse deformation parameters along with the main model parameters with respect to the standard clinical variables, one can create simplified models that are easy to fit to the patient data and provide a clear visual and objective assessment of cardiomyopathies. This information will be integrated with the simulation results for a comprehensive picture of the individual patient.

B.1.2.1.2 Cardiovascular Disease Risk in Obese Children and Adolescents

Obesity is commonly acknowledged as a major risk factor for cardiovascular disease (CVD). However, the precise mechanism leading to the development of cardiovascular risk in obesity from childhood to adolescence to adulthood remains largely unsolved. In particular, it is still unclear whether childhood obesity increases CVD risk simply because of the tracking of obesity from childhood to adulthood or via the development of CVD risk factors already present in childhood and adolescence. Many structural and functional changes in the adolescent heart, such as left ventricular (LV) hypertrophy, left atrial (LA) enlargement, and subclinical impairment of LV systolic and diastolic function are believed to be precursors to more overt forms of cardiac dysfunction and heart failure.

In order to rate the degree of obesity for clinical diagnostics and studies, the body mass index (BMI) is still the primary measure, also in children. However, BMI only estimates the general adiposity of a subject, while it does not take into account the distribution of adipose tissue within the body. Specifically, visceral adipose tissue (VAT), the fat between the abdominal organs, has shown to correlate highly with CVD. In addition, subjects with normal BMI may still have high body fat content, which has proved to be a significant CVD risk factor for adults.

Complementary to BMI, imaging modalities such as computed tomography (CT) or magnetic resonance imaging (MRI) allow measuring specific adipose tissue types and have established themselves as important tools for diagnosis (Figure 7). While CT and MRI are the current gold standard for adipose tissue quantification, high costs (and the radiation exposure of CT) restrict these modalities to large-scale studies, and ultrasound

(US) is becoming an affordable, non-invasive alternative. .

In order to decrease the manual workload of the operators, several methods have been proposed for semi- or completely automated image-based quantification of adiposity. The extraction of adipose tissue from MRI has been studied extensively, either for selected body regions or for whole-body scans. Since adipose tissue features high intensities in MRI, many authors use thresholding to separate it from the

Figure 7 Amount and distribution of adipose tissue can be quantified accurately from MRI.

Figure 6 Statistical model of right ventricle remodeling in tetralogy of Fallot [Mansi 2011]

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surrounding tissue. Although an automatic selection of thresholds has been proposed, different adipose tissue types (VAT and subcutaneous adipose tissue, SAT) still have to be separated manually. An automatic algorithm for this problem was developed, based on an active contour algorithm. Liou et al. proposed to use morphological operations, edge detection, and knowledge-based curvature fitting. In all these approaches, bone marrow is often misclassified as adipose tissue, because it features similar intensities in MRI. Thomas et al. excluded bone marrow by user interaction, while Shen et al. eliminated the paravertebral adiposity tissue automatically. Kullberg and al used geometrical models of the pelvis and vertebra to exclude these structures and thresholding and morphological operations to automatically separate VAT and SAT. Zhou et al. employed fuzzy c-means clustering and thresholding to quantify VAT and SAT in both water-saturated and non-water saturated MR images.

In MD-Paedigree, we will re-use our proven anatomical organ models developed in Health-e-Child and Sim-e-Child to add prior knowledge to image analysis. This will enable us to assess different adipose tissue types automatically from image data and use this information in our further analysis.

In addition to the fat distribution data from imaging, we will also use established biomarkers such as blood pressure, metabolic and haemodynamic data to estimate the CVD risk. Currently, most studies that analyse different factors of CVD risk employ univariate or, at best, multivariate but linear models, which represent a major limitation. Univariate models can only identify independent contributors to the risk, while they do not shed much light on the interplay between the factors. As demonstrated by cardiovascular risk can be modelled by multivariate machine learning models with only ten clinical variables (representing commonly acknowledged markers of CVD risk). In a similar study, Kurt et al. successfully modelled the risk of coronary artery disease with a multi-layer perceptron (MLP) and a comparable set of 8 clinical variables. Sumathi and Santhakumaran trained an Artificial Neural Network (ANN) on a set of 15 clinical variables and claimed to use it successfully for early diagnosis of hypertension. In MD-Paedigree, we will construct multivariate non-linear models of CVD risk involving state-of-the-art statistical and machine learning techniques. This will not only help to build more accurate models of CVD risk, but also to better understand the mechanism of CVD development via the identification of important risk factors and understanding of their interrelation. Such personalised risk models may become a more reliable alternative or at least a useful complement to the CVD risk prediction charts of WHO, especially since these charts are available for adults only.

A common drawback of the existing works of multivariate modelling is that the underlying techniques like Multi-layer-Perceptron (MLP) or Artificial Neuron Networks (ANN) are basically “black box” models, i.e. the reasons for their results cannot be conveyed to their human users, which leads to low acceptance rates among clinicians. In our modelling, we will focus on case-based reasoning and discriminative distance learning instead. Since these systems base their decisions on concrete patient cases and are able to present the relevant cases (i.e. the ones utilised for decision making) to the user, they provide easy and intuitive decision support and a possibility for personalised therapy planning, based on the clinical history of retrieved similar patients.

Our work will be centred on the similarity search based decision support system HeC CaseReasoner developed in the Health-e-Child project. It features recently suggested techniques for discriminative distance learning, including learning from equivalence constraints and the intrinsic random forest similarity.

The basic philosophy behind the design of CaseReasoner is to provide clinicians with a flexible and interactive tool to enable operations such as data filtering and similarity search over a grid of clinical centres, and to facilitate the exploration of the resulting data sets. The major aim is to let clinicians explore and compare the patients’ records, regardless of geographical location, and to visualize their place in the distribution of both the whole population of patients, as well as in the distribution of its semantic subsets (Figure 8). The search platform can then be used for several tasks such as case-based retrieval, support for curation and ultimately decision support. HeC CaseReasoner employs a domain-independent technology, and has been applied within Health-e-Child for decision support in three domains: cardiology, neuro-oncology, and rheumatology. With MD-Paedigree, HeC CaseReasoner will be further extended and applied to decision support in the domain of modelling cardiovascular risk in obese children and adolescents.

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In summary, our major objectives with modelling the cardiovascular risk in the obese child and adolescent are (1) automated, objective quantification of different adipose tissue types and their distribution from MRI and ultrasound data, (2) collection of a large number of additional factors contributing to the risk, including metabolic and haemodynamic factors, clinical and family histories, and their interrelation, (3) construction of personalised multivariate retrieval-based models for the assessment of cardiovascular risk using state-of-the-art machine learning techniques, both for cross-sectional and longitudinal studies, (4) interpretation of the models with the purpose of better understanding the mechanism of cardiovascular dysfunction from childhood to adolescence and adulthood, and quantitative evaluation of their predictive performance with cross-validation and sensitivity analysis, and with evaluation on unseen subsequently acquired cases.

B.1.2.1.3 JIA

In the frame of the EU FP6 Health-e-Child project, a great deal of effort had been spent in order to standardise imaging procedures and devise paediatric-targeted scoring systems for the assessment of disease activity and damage in JIA considering the wrist. The collaboration between clinical and IT partners has enabled the development and validation of computerized quantitative measurements of inflammation and destructive changes that have shown potential value as predictors of future damage .

In continuity from the work developed in Health-e-Child, which has led to advanced personalised modelling of disease progression, the goal will be to implement a more robust multi-scale, personalised and predictive computer-based model of JIA – this time focusing on a wider range of joints than the wrist joint. It will span body, organ, tissue and molecular level with adequate information fusion and in addition information obtained from gait analysis. This allows for pattern discovery in multimodal data through correlations between clinical data, imaging, immunological, metagenomic data (gut microbiota), and a biomechanical gait model.

The driving force behind this project stems from the integration of data coming from a new cohort of patients (approximately 180 patients) into the framework developed within the Health-e-child project that will be further extended and adapted to the needs of MD-Paedigree. Initial imaging will be performed at disease onset and followed for 1-2 years,, in order to expand predictive multi-scale models in JIA. The longitudinal design of the study will allow a dynamic process of testing multi-scale disease models for each patient at follow-up visits to further personalise treatment strategies.

Imaging of the Affected Joints

By fusing the information on the anatomy and the physical properties of the tissues provided by the imaging technologies, with the functional information provided by the CGA, it will be possible to personalise a whole body-level model of the musculoskeletal dynamics capable of predicting the forces acting on a given joint during the patient movements. These forces will then be applied to an organ-level finite element model of the joint, where the mechanical properties of the tissues will be informed as much as possible from the imaging data. Among the other things we shall explore the possibility to derive cancellous bone anisotropy from DTI-like MRI imaging, mechanical properties of the cartilage from

Figure 8 CaseReasoner uses a relative neighbourhood graph in order to assist in visualizing proximity and treatment decision support for a cohort of cardiac patients within Health-e-Child. Patients’ pulmonary trunks are displayed at the nodes of the graph.

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distribution of the GAG content again obtained by MRI, etc. We shall also correlate the biomechanical predictions with the signatures of the disease that can be quantified, such as the extension and the location of the cartilage erosion, or the alteration of the subchondral bone, to the predictions of stress and strains obtained by the organ-level model.

As shown in literature a combination of MRI and US imaging is beneficial for the assessment of JIA. High-resolution US will be performed not only at the joints (ankle) investigated with MRI, but at all the affected joints, in order to better define the extent of the disease. The severity of joint involvement will be judged sonographically by a variety of parameters such as joint effusion, synovial thickening and hyperaemia, cartilage integrity and bone erosions. Quantitative assessments of these parameters will be extracted from the US equipment based on standardized scanning planes by means of 2D imaging. At the same time, using 3D imaging, serial slices will be recorded resulting in a pyramid-shaped volume scan. The acquisition and storage of a number of volume datasets with time would allow better comparison of findings in longitudinal studies and the detection of earlier and subtle predictive signs of damage.

The lower limb MRI will allow extraction of a wide range of parameters, otherwise not available, that will enhance the musculoskeletal modelling portions of WP10. For example: muscle volume measurements will allow more precise determination of each muscle’s tetanic force, the 3D nature of the MRI data will allow a more accurate estimate of joint centres and allow precise location of muscle origins, insertions and paths as well as increasing the accuracy of the estimations of the inertial properties of the lower limb segments.

All these personalised models will be composed in an integrative multiscale representation of the patient’s musculoskeletal system, capable of predicting, for example, the forces being transferred to the joint cartilage during a given movement as captured during the gait analysis.

Articulated Modelling of the Affected Joints for Automated Biomarker Extraction

The progress beyond the Health-e-Child project is defined by clinical as well as technical aspects. The wrist MRI scores, as well as the automated software for the quantitative assessment of disease activity and damage, developed in the frame of Health-e-Child, will be adapted to investigate the ankle. Focusing on the locomotory system, especially the juvenile ankle, enables the physician to study the effects of JIA on the joint motion, which form another scale in the patient-specific model. MD-Peadigree aims to automate and extend the multimodal image analysis and therefore, standardise the derived biomarkers by means of model-based segmentation of MRI images. For this purpose, an articulated model of the juvenile ankle and wrist will be developed and used. It includes the bones’ shape, the spatial relation between the bones and their appearance in MRI images. By simulating the joint articulation, it will allow for the adaption to a specific MRI-scan, resulting in patient-specific models.

In order to generate a personalised morphological model for JIA, an articulated joint model – consisting of bones, cartilage and ligaments representing the variation in shape, image appearance and spatial relations trained using machine learning methods – will be developed. It will be built from manual annotations by experts on morphological MRI datasets of patients suffering from JIA. Data from MRI molecular imaging analyses will be also included as well as data from US evaluation.

Musculoskeletal Modelling of the Joint Kinetics

Furthermore, the role of the musculoskeletal dynamics and of the mechanical properties of the joint tissues in conditioning disease progression or in response to treatment will be investigated. The integration of image based patient-specific models with gait cycle analysis will allow the generation of highly personalised multiscale models of the musculoskeletal system capable of elucidating the role of biomechanical properties in onset and/or progression of structural damages. Three-dimensional clinical gait analysis (CGA) is a well-established method enabling, when a strict analysis of causes of errors is carried out and periodical validation procedures are implemented (see for more details the paragraph

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Neurological and Neuro-muscular Diseases (NND) - Progress beyond the State-of-the-Art) highly objective and reliable evaluation of gait in both healthy and diseased populations. CGA including kinematics and kinetics, provide more information about gait changes, such as joint angles and moments, which cannot be quantified in a standard clinical setting. The kinematics shows the joint movement, while the kinetics describes the forces involved in movement (e.g. ground reaction forces, joint moments, and joint powers). By examining kinetics, the mechanisms of gait deviation can be described and the early use of gait analysis can be instrumental in discovering developments of potentially destructive gait deviations. Patients will be dressed with skin-attached markers that are both visible in MRI imaging, radiopaque (and, successively, reflective markers will be reapplied in the same anatomical positions, so they can be tracked during gait analysis. Whole body imaging and gait analysis will be performed one after the other with the patient dressed with the markers. This will provide a fiducial registration framework between anatomical and functional data. The imaging protocol will be agreed with the modellers, in order to ensure that the highest amount of information is transferred to the predictive models.

Each patient will be examined using three-dimensional clinical gait analysis (CGA), ground force platform, and cutaneous electromyography (EMG). Depending on the joint of interest, the patient will be asked to repeat a few times a given movement, selected among those most common in daily life (i.e. for lower limb, level walking, stair climbing, sit to stand, etc.), and the relative motions and muscle activation signals are recorded. An expert physiatrist will examine the gait analysis data to exclude specific gait abnormalities.

Using the fiducial marker set, the motion data will be fused with the imaging data, and with the internal, musculoskeletal, and joint models fitted to the imaging data. This will result in a body-organ multi-scale model capable of predicting the forces being transferred to the joint during each of the recorded movements. EMG data will not be used to inform the model, but will be compared with the activation patterns predicted by the models, so as to verify that the model is operating consistently with the patient’s neuromuscular activation strategy.

The body model will use inverse kinematics to find the optimal registration framework between the model and the recorded kinematics, so as to reduce as much as possible the so-called skin artefacts. Then, inverse dynamics will be used to compute the joints torque that is required to generate the recorded movement. An optimisation scheme will be used to compute muscle activations and joint forces. This time-varying system of musculo-articular forces will be applied as boundary condition to a finite element model of the joint being investigated. The individualised finite element model will predict the mechanical stresses and strains induced in the various joint tissues by the given movement, and information to be used as an additional “biomarker” in the evaluation of the individual clinical case.

Immunological Analysis

Imaging data will be integrated with immunological and metagenomic data in order to try to identify surrogate parameters for disease activity, disease severity, risk of side effects and treatment outcomes. New particle-based multiplex immunoassay, such as the Luminex technology, allowing the measurement of multiple circulating and/or synovial cytokines, as well as of other immune mediators, will be used to define the individual immunological profile for each patient. Furthermore, paired peripheral blood and synovial fluid mononuclear cells subpopulations (naive and effectors T cells, B cells, monocytes, etc.) will be evaluated by cytofluorimetric analysis. We will also look at phenotypic markers, mRNA, epigenetic markers (methylation FOXP3) and functionality (in vitro suppression assays).

Analysis of gut microbiota (the genome of microbes present in the gastrointestinal tract) will provide new insight into the environmental factors which regulate innate and adaptive immune homeostasis and affect the development of systemic autoimmune diseases. The gastrointestinal tract is the largest human immune organ and home to a complex community of trillions of bacteria that are engaged in a dynamic interaction with the host immune system. (The human body contains over 10 times more microbial cells than human cells). Communication between the microbiota and the host establishes and maintains

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immune homeostasis, enabling protective immune responses against pathogens while preventing adverse inflammatory responses to harmless commensal microbes. Correlations have been found between the composition of gut microbiota and some preferential immune responses (i.e. Th17 response). By analysing the gut microbiota of JIA patients collected in specific disease states (at the onset, when patient will achieve clinical remission state, and during flare of the disease) we aim to explore its potential role in conditioning disease susceptibility as well as immune response in the different stages of disease, thus adding a further important dimension to multiscale analysis. Investigating the interaction of gut microbes and the host immune system will improve the understanding of the pathogenesis of this autoimmune disease, and provide innovative foundations for the design of novel immuno- or microbe-based therapies.

Prediction of the Disease Course

The impact of biomechanical property alterations on subsequent progression of structural damage in patients with chronic inflammatory arthritis is not yet characterised. Personalised joint biomechanical modeling allows critical evaluation of the forces within the joint under physiologic and pathological loading conditions. Evaluation of the impact of joint mechanical abnormalities on disease progression is needed for an accurate outcome prediction. The potential of the multi-scale modeling methods proposed, is to enable the exploration of complex systemic interactions between the neuromuscular control, the musculoskeletal functional anatomy, and the local biomechanical determinants acting in the joint space at the tissue level.

The modelling predictions could have significant implications in early diagnosis and therapeutic intervention. In this perspective, early signs of structural damage will be evaluated also using MRI molecular imaging analysis. Molecular imaging allows the detection of microstructural changes in the composition of the cartilage matrix that occurs before morphologic changes can be qualitatively detected by conventional imaging, at stages when damage to the cartilage is potentially still reversible and may be treated. Molecular imaging by providing in vivo information beyond morphological changes in articular cartilage, might yield attractive new insights in the biological pathways of cartilage turnover, with the potential to improve our understanding on erosive disease mechanisms and disclose new targets for therapy, thus suggesting a potential role for MRI in the drug development process.

Demographic clinical imaging and laboratory data in the form of text, images, annotations, videos, biomarkers and articulated models will be entered in the MD-Paedigree digital repository and will be continuously analysed providing potentially more accurate disease model tools. The combination of different assessment techniques will enable to enhance the value of a multidisciplinary management of JIA.

The multidimensionality of the human and microbial phenotypes (and the dynamic, nonlinear inter-actions) will be explored by means of improved informatics tools, including new approaches for understanding the complexity of the metadata, in order to better understand the implications of gut microbiota variations in human health and disease.

The prognostic value on an individual level of multidimensional data, including modern imaging modalities, immunological, metagenomic data, as well as articulated models and biomechanical models will be explored.

JIA constitutes an ideal domain for assessing the merits of simulators and predictors based on data generated across different scales. The validity and effectiveness of the proposed solutions will be assessed by using the model to address several open issues in JIA with a strong clinical impact on early diagnosis, prediction of disease and of treatment outcome.

B.1.2.1.4 NND: protocols and personalised models in Advanced Clinical Gait Analysis

To reiterate the conclusion of the NDD clinical background section: the potential of gait analysis to serve clinical decision making in NDD is generally under-used for several reasons. These will be taken up within the MD-Paedigree project.

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Protocol definitions for clinical gait analysis

Three levels of protocol definitions are needed to assure multicentre reliable data for the repository:

Technical Quality assurance for CGA laboratories

It is important to realise that for accurate data from the experimental systems a strict analysis of causes of errors and periodical validation procedures needs to be implemented in the gait labs. If the adopted experimental procedure permits the gathering of valid data, the first important prerequisite for reliable and accurate results from a particular subject is fulfilled. Within MD-Paedigree these quality assurance (QA) procedures will therefore be formalised between laboratories for clinical gait analysis. MD-Paedigree will constitute a European standard for technical QA and have this approved by the important European bodies on clinical gait analysis, i.e. the ESMAC. A consensus meeting will be part of this.

Standardisations of gait analysis protocols: Marker placements

One of the main non-technical sources of error in CGA using OptoElectronic Movement Analysis systems is caused by marker artefacts, resulting from skin movement relative to the bone. Recently it has been shown that, in the case of well-trained staff, errors due to marker placements errors and skin movement artefacts will stay within a few degrees of error of the joint kinematics graphs. This error level is considered to be just clinically acceptable. This means that all gait labs should fulfil the requirements to be qualified for MD-Paedigree graded gait analysis. In analogy with the Technical Quality Assurance (TQA), MD-Paedigree will strongly promote interoperability and constitute a protocol for standardised marker placement, as well as standard procedures to evaluate this within and between laboratories. In parallel, we shall explore the possibility to use imaging/gait analysis protocols, where patients are dressed with radiopaque/MRI opaque and reflective markers attached to the skin as used in gait analysis protocols, while the imaging protocol is conducted. These data will make possible to use sophisticated inverse kinematics modelling methods to minimise the skin artefacts, and to obtain accurate estimations of the skeletal kinematics.

Standardisations of gait analysis protocols: operational protocols

The results of kinematics and kinetics of CGA are also dependent on the use of standard protocols for instruction on walking targets. In particular, the enforcement of a precise walking speed is of major influence on the output. As such, instructions should be carefully standardised and protocols developed that use multiple walking speeds. It has been suggested and shown by previous studies that these protocols are necessary to detect important pathological features of the NMSS of the subject, especially in patients with CP. EMG recordings and oxygen consumption will be part of the overall assessment procedures. Moreover, in order to feed the development of probabilistic models a standardised description of therapies will be completed. This description will be used to longitudinally describe the applied clinical workflows that are currently used to improve gait performance in children with NND.

Conclusion: the established and clinically authorised protocols (technical, marker and procedures) of CGA will be an important step forward for the NND paediatric care in the EU, along with the establishment of a reliable MD-Paedigree database for typically developing children.

Application of computational biophysical models of the NMSS in CGA

For clinical gait analysis the use of Neuro-Musculo-Skeletal (NMS) models is an important step forward in the interpretation of its results, aiming to inform the clinical decision-making. Because of the modelling based interpretation, the physician no longer needs to interpret the results of clinical gait analysis, within his own informal frame of interpretation. Using NMS models the results of CGA are quantitatively

Figure 9 Personalised and Integrative musculoskeletal model

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"translated" into the function and performance of the underlying structures, i.e. muscle activation, muscle forces, and joint loads that make possible to unravel the aetiology of the pathological gait pattern of the subject under study.

The EU project “Personalised models of the Neuro‐Musculo‐Skeletal Physiome” (NMS Physiome) is moving towards the development of PPI (Predictive, Personalised and Integrative) musculoskeletal medicine. NMS Physiome is a part of the European Union’s Virtual Physiological Human initiative.

A key result of this project, conducted by Prof. Viceconti, at MD-Paedigree partner USFD, is the integration of an advanced software application for the pre-processing of imaging and gait analysis data into a full musculoskeletal model (NMS Builder) and the OpenSIM musculoskeletal modelling environment developed by Stanford University. NMS Builder is already available in prototypical form to all partners of the MD-Paedigree consortium.

Although NMS computational models are thus well known in the biomechanical research community, as yet only one company, MOTEK, has incorporated gait analysis and model based interpretation of gait for market delivery. Their model (the HBM model) is computationally very efficient: even without high performance computers it can run in real time. More complex modelling activities can be conducted using the NMS Physiome tools.

The actual problem of accuracy of NMS models is that all models currently used in paediatric gait analysis are based on data scaled from a single cadaver in a simple way. Sensitivity studies have shown that such a gross simplification in applying generic models is too inaccurate, and, especially in the case of children, dedicated and validated models, fused with medical imaging data, should be developed in order to yield reasonable accuracy for clinical application in this population.

The first level of MS models in CGA is the mass distribution model of body segments. Mass distribution means that the masses, centre of mass and inertial properties of each segment need to be known for accurate calculation of inverse dynamics resulting in valid joint kinetics. What is needed is a method for scaling that allows application, in clinical workflows, to enable personalised medicine. MD-Paedigree will develop and evaluate a scaling method for the NMSS of children, to be applied in existing NMS models that are used in CGA. Validation will be based on MRI measures.

The second level of personalised MS models in CGA are to account for the subject specific bony deformities. The bony deformities that should be accounted for can be limited to the clinically well known deformities in CP. These deformities have significant influence on the output of NMS model calculations (i.e. femoral anteversion and tibial torsion). These effects could primary be modelled by morphing the generalised bony structures towards the actual morphology of the bone. The most important effects of bony deformities should be parameterized by the effects on axis alignment: (a) introducing a skewness of the principal axes of rotation of the joints in the kinematic chain of linked segments, and (b) the altered lever arms of muscles with respect to these principal axes of rotation of the joint. Again antropometric measures will be explored.

The third level of personalised modelling is to account for pathology specific muscle parameters. These models should focus on the parameters that are known to be of large influence on the second step in inverse dynamics, i.e. the estimation of muscle forces based on optimisation criteria on how to explain the net joints moments from CGA. This means that especially muscle contractures, altered muscle structure and hypertonia (in CP) as well as muscle weakening (in DMD and SMA) must be targeted. US measures of the muscle belly, along with fibre directions will enable estimates of the muscle Physiological Cross sectional Area (PSCA), while dynamometric evaluations will yield measures of muscle belly length and optimal fibre length.

Supporting probabilistic models, despite the strong potential of biophysical models of the NMSS, will only hold a certain amount of predictive value, i.e. as far as their assumed accuracy will allow. However, in clinical practice, even if the pathology cannot be fully explained by biophysical modelling, the use of probabilistic models is still extremely powerful in supporting clinical decision making. Until now only two gait laboratories in the world (Gillette Children’s, Minneapolis, US and Pellenberg, Leuven, Belgium)

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have explored the possibilities of generating decision rules from their dataset. These laboratories are the only ones that have created a large enough set of reliable data to make such an effort worthwhile. In MD-Paedigree the clinical partners will collect data, according to the dataset and quality protocols defined on the basis of standardised formats, for feeding into the repository.

B.1.2.2 Description of MD-Paedigree’s modelling and data baseline

B.1.2.2.1 Baseline in terms of re-use of models already available in the consortium

Summarising different elements of information scattered in various parts of this section, some fundamental components of the re-use approach which characterises MD-Paedigree, and which constitutes the modelling baseline of the project, can be highlighted according to the following partition:

1) With regard to the 2 cardiac studies, MD-Paedigree re-uses:

The models and simulations developed in Health-e-Child and Sim-e-Child, extending them to Cardiomyopathies and CVD Risk,

• merging all scattered information obtained from different diagnostic tools in clinical practice

• obtaining a generative model of heart function in children

• where aspects of the arterial circulation are integrated as boundary conditions and modelled using with visco-elastic walls

Also CaseReasoner, the support system developed in Health-e-Child providing a flexible and interactive tool for data filtering and similarity search over a grid of clinical centres facilitating the exploration of the resulting data sets, is re-used and further extended, to all the other diseases which are taken into account in MD-Paedigree, and is applied, in particular, to decision support in the domain of modelling cardiovascular risk in obese children and adolescents.

2) With regard to JIA, MD-Paedigree re-uses:

The personalised and predictive computer-based model of JIA, already developed in Health-e-Child, is expanded focussing on:

• a wider range of joints, adapting the initial methodology for scoring wrist MRIs, as well as the automated software for the quantitative assessment of disease activity and damage, to investigate the ankle,

• making use also of high resolution Ultrasound Imaging spanning body, organ, tissue and molecular level with adequate information fusion and in addition information obtained from gait analysis (Motek model),

• allowing for pattern discovery in multimodal data through correlations between clinical data, imaging, immunological, metagenomic data (micro-biota), and a biomechanical gait model.

• exploring complex systemic interactions between the neuromuscular control, the musculoskeletal functional anatomy, and the local biomechanical determinants acting in the joint space at the tissue level by detecting early signs of microstructural changes in the composition of the cartilage matrix also using MRI molecular imaging analysis.

3) With regard to both JIA and NND, MD-Paedigree re-uses:

The HBM Motek model for real time clinical gait analysis (CGA):

• to assess the function and performance of the underlying muscle activation, muscle forces, and joint loads in order to unravel the aetiology of the pathological gait pattern of the patients,

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• in combination with:

o inverse kinematics modelling methods to obtain accurate estimations of the skeletal kinematics,

o EMG and oxygen consumption recordings.

o Computed Tomography (HRpQCT).

• Some of the patients will be examined also with MRI at the same site, and the tissue orientation computed from DTI-like processing of the MRI images, to be verified against the HRpQCT data

• The VPH tools developed within FP7 NMS Physiome project (derived from the EC funded VPHOP and the NIH funded SIMBIOS) will also be re-used by MD-Paedigree, integrating the pre-processing of imaging and gait analysis data into a full musculoskeletal model through NMS Builder and the OpenSIM musculoskeletal modelling environment.

MD-Paedigree explores the potential of gait analysis in combination with the extraction of subject-specific bone and muscle anatomy from MRI, having as goal the fusion of multimodal sources of data (MRI, Muscular US, EMG and CGA), in order to develop correlative explorations between:

• clinical signatures of the diseases that can be quantified using clinical, imaging, or instrumental assessment,

• and the prediction of the biomechanical models, as a support for the ethiopatological speculation (JIA) and a more effective scoring of the disease severity and for treatment planning (NND),

All image processing and image modelling methods will be tested using an alternative source of information, typically CT scans, to validate bone reconstruction.

B.1.2.2.2 Baseline in terms of data already acquired to the consortium, and additional data collection

The data collection performed within Health-e-Child and Sim-e-Child is still available to the MD-Paedigree consortium thanks to the continuity in the eHealth platform which is shared by all three projects. In addition, the new patients’ recruitment to be perfomed within MD-Paedigree consists of: Cardiomyopathies: 180 children, within month 30: sixty patients (among which 30 girls) for each clinical centre. CVD risk in obese children: 180 patients , within month 36: sixty (among which 30 girls) for each clinical centre. JIA: Altogether 180 patients within the month 34. NND: In this area the three disease analysed will imply different forms of patient recruitment:

1.CP: 50 patients for each clinical centre for probabilistic modelling (+ altogether 600 retrospective patients from KU Leuven and OPBG.

2.SMA

• 20 ambulant patients (severity grade type 3)

• 10 patients for each centre for biophysical modelling

• 10 patients among the 3a subgroup (symptoms of weakness appearing before age 3 years)

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• 10 patients among the 3b subgroup (weakness appearing after the age of 3 years

3.DMD

• Clinical data will be collected by OPBG, KU Leuven and VUA from 20 ambulant genetically confirmed DMD Patients

• 10 patients with an age ranging between 5 and 7 years

• additional 10 patients with an age ranging between 8 and 11 years. Furthermore, MD-Paedigree is going to perform DTI processing for muscle and bone, re-using data collected at USFD validated on a small cohort of patients that are undergoing wrist or ankle HRpQCT as true value. Genetic and meta-genomic: Samples will be collected from 180 patients for cardiomiopathies, 180 for CVD risk in obesity, 180 for rheumatology, and from a control group of 100 unaffected subjects.

B.1.2.3 The MD-Paedigree VPH Infostructure and Digital Repository

Building on the Sim-e-Child/PCDR digital repository (http://sec-portal.maatg.fr/), MD-Paedigree will implement the Service-Oriented Knowledge Utility (SOKU) vision, to facilitate the design and development of innovative new predictive models as reusable and adaptable workflows of data mining applications, and turning the latter into clinically validated decision support tools, made available at the point of care to physicians.

B.1.2.3.1 Service-Oriented Knowledge Utility (SOKU)

MD-Paedigree translates the domain-specific applications and data into services and associated knowledge that can be further published, discovered and semi-automatically orchestrated in the grid/cloud, by physicians and medical data integration experts. This way leads to the development of new workflows and enables their personalisation to real patient cases. The MD-Paedigree system consists of standard services that have various levels of knowledge awareness, starting from basic utilities under the form of classical Web services wrapping up computational resources, to hybrid services that only consume data and information, and to finally more complex high-level entities, which produce knowledge. The semantic enrichment of services in the platform enables the system to understand its own constitution and to provide users with guidance in a variety of options compatible with the defined execution context (i.e. patient data, applications, objective, etc.). MD-Paedigree exploits recent ground-breaking European research on semantic modelling, ontology-based data access and scalable query execution to develop an extensible platform based on open standards and protocols to deliver a complete and generic solution able to tackle the targeted paediatric disease areas, while remaining adaptable and evolvable to additional disorders in the future. This is what the following sections further elaborate on.

B.1.2.3.2 Data Access and Query Formulation

One important goal of the MD-Paedigree infostructure is to provide the necessary tools and applications to assist users in accessing and foraging the wealth of heterogeneous data available in the digital repository in an easy, intuitive and seamless way across the care continuum via enhanced connectivity with other hospital information systems and the patient’s electronic health records. Advanced techniques, tools and languages for accessing federated data are thus used (both machine- and user- oriented). Technologies and research related to Ontology-Based Data Access (OBDA) are applied, such as the new forms of query by navigation based on ontologies i and the extensible declarative query language supporting linked data (e.g. SPARQL endpoint). Interactive search based on relevance feedback will be applied to improve data recall in the infostructure.

http://sec-portal.maatg.fr/

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B.1.2.3.3 Distributed Processing and GPU support

MD-Paedigree extends the distributed processing capabilities of the Sim-e-Child platform in two major axes. On the one hand, it develops compatibility with GPU processing and makes it possible to execute validated models onto real patient data, thus providing real-time support to physicians at the point of care in the 7 participating centres. Indeed, the introduction of non graphics application programming interfaces (APIs) for GPUs brought a new perspective on GPUs, transforming them into general purpose units. On the other hand, MD-Paedigree will experiment with the operation of a sustainable translational service for healthcare professionals and other external centres, by integrating an open Cloud API (i.e. the OCCI) in its abstraction layer, thereby allowing the infrastructure to elastically adapt according to faced requests from end-users. The ATHENA Distributed Processing (ADP) Engine is considered to more easily integrate and adapt algorithms distribution, through the newly integrated abstraction APIs.

B.1.2.3.4 Intelligent Mining, Modelling, Reasoning and Simulation Framework

MD-Paedigree integrates AITION, an evolutionary information processing and knowledge discovery framework developed by the “ATHENA research and innovation center in information communication & knowledge technologies” (ATHENA) for biomedical research, which is able to provide highly accurate predictive and statistical simulation models combining (1) a bottom-up data-driven process to analyse heterogeneous demographic, phenotypic, clinical, molecular, and genomic biomedical data, images and streams; and (2) a top-down model-driven process to incorporate external knowledge coming from domain experts, literature, or model-guided processes and relational/semantic models. AITION integrates Probabilistic Graphical Models (PGMs) as a unifying patient/disease modelling approach providing an integrated framework for multi-scale vertical integration, feature selection, simulation, knowledge discovery and decision support. Initially developed and tested in the Health-e-Child project, AITION is based on state-of-the-art techniques for Bayesian Network Learning, Markov Blanket induction and real-time inference.

Moreover, ontologies and a priori knowledge will also be incorporated automating causal discovery and feature selection, providing semantic modelling under uncertainty. In MD-Paedigree, hierarchical architectures,

Figure 10: Illustration of AITION Framework

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as well as, Granular Computing (GrC) and Statistical Relational Learning (SRL) techniques will be extended. SRL is an emerging research area which aims at combining statistical learning and probabilistic reasoning (such as PGMs) within logical/relational representations providing multi-entity reasoning for complex situations involving a variety of objects, as well as relations among them. SRL allows overcoming assumptions of traditional Machine Learning propositional approaches and i.i.d. assumptions, while making it possible to capture both uncertainty and similarity. Moreover, the Hierarchical Layered Architecture incorporating hidden (latent) layers/variables and GrC techniques allows to build an efficient multi-resolution computational model targeting complex applications consuming large amounts of data, information and knowledge. MD-Paedigree will thus deliver mathematically and semantically well-grounded, scalable, dynamic, hierarchical statistical simulation models that will allow efficient Bayesian inference and online learning addressing multi-entity, multi-modal, high-dimensional spatial data analysis and temporal reasoning over the distributed infrastructure.

B.1.2.3.5 Holistic Model-Guided Personalised Medicine

Ultimately, MD-Paedigree will provide an evolvable framework for holistic model-driven medicine and personalised treatment combining knowledge constructs from observational data analysis, statistical and specialized VPH patient- or disease-specific simulation models, domain knowledge representations, as well as patient/disease-specific profiles.

The goal will be to find efficient ways to optimise and combine multiple statistical and/or specialized VPH simulation models in prediction tasks supporting the creation and validation of model-driven clinical workflows. Utilizing the PAROS personalisation platform, clinicians and domain experts will create ontology-based patient and disease-specific profiles capturing high-level concepts and common characteristics. Similarity search techniques will then be developed mapping specific medical cases to pertinent patient/disease profiles. These profiles will be used to adapt and optimise individual simulation models by transformations, as well as to explore their combinations and re-use in different disease areas. Finally, a holistic scheme for model-driven personalised medicine will be developed that will allow analysing and testing scientific hypotheses, predicting disease evolution and treatment responses (e.g. early diagnosis of poor outcome that needs aggressive treatment) and elaborating individualized treatment plans. This outline is illustrated in Figure 11.

B.1.2.3.6 Compliance with Guidelines for Model Based-Drug Development (MBDD)

Taking into account the need to demonstrate model robustness and reusability, also by complying with the guidelines for future clinical trials design and execution, will in fact imply devoting special attention to having functional databases available to assist drug developers. Indeed, with the advent of molecular biology coupled with advances in screening and synthetic chemistry technologies, a combination of both random screening and knowledge around the receptor is used for drug discovery. The complexity of the discovery pipelines is becoming greater and greater as medicinal chemistry meets with personalised medicine not only to design new drugs but also new diagnosis procedures.

MD-Paedigree can support the drug discovery process. In particular, it can help in identifying biomarkers

Figure 11: Illustration of Model Guided Personalised Medicine Process

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likely to characterise a particular pathology or dysfunction. By modelling the complex process of a particular disease and clinical intervention associated to healthcare (e.g. drug prescription, morbidities, diagnostic procedures…), the project knowledge bases can help identify specific biomarkers (such as vital signs, phenotypes, protein…). Second, it can help to design clinical trial protocols (i.e. exclusion/inclusion criteria, statistical power, and cohort identification) by providing a feasibility testbed to conduct clinical research studies, as currently explored by IMI projects such as EHR4CR.

Last but not least, the longitudinal follow up of MD-Paedigree populations can help to monitor longer-term effects of therapeutic treatments, including -drug response, phenotype evolution (e.g. neoplastic processes), as well as rare adverse effects. The resulting views can ultimately help to cluster populations according to specific genotypic variations (pharmacogenomics).

Figure 12: MD-Paedigree Infostructure

B.1.2.4 MD-Paedigree’s Infostructure Baseline With regard to its Infostructure, MD-Paedigree’s baseline hings mainly on previous FP6 and FP7 projects, leading it to re-use:

The grid and cloud-based eHealth repositories developed in Health-e-Child and Sim-e-Child

Building on top of current developments in cardiology within the OPBG (the Paediatric Cardiac Digital Repository -PCDR) in cooperation with Maat

MD-Paedigree co-operates with VPH Share and, through it, with p-Medicine, to insure:

the usefulness of MD-Paedigree for the entire VPH community

interoperability at the semantic layer for effective indexing and search of the data collections .

MD-Paedigree leverages, through Siemens and HES-SO, semantic web technologies in healthcare originating also from the THESEUS-MEDICO research consortium, funded by the German Ministry of Economy, and the FP7 Medical Analysis and Information retrieval project KhreshMoi.

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MD-Paedigree pays special attention also to the goal of ensuring the models’ external re-usability by enhancing user and VPH community acceptance, by providing Appropriate tools and mechanisms for usability, accessibility and maintenance of the system, and

by using, among others, open environments and open-source software

Proof of concept of re-use of models within MD-Paedigree and throughout the project’s lifetime :

• between disease areas, to leverage synergies whenever possible,

• managing the requirements for the model-driven Infostructure, in collaboration also with external stakeholders, to ensure that MD-Paedigree is an integral part of the EU data infrastructures ecosystem, dealing with data accessibility, interoperability and exchange, and remains open to dynamically evolve through an ongoing process of validation.

Re-usable VPH models are incubated in the system with progressive semantic enrichment and model transformations in a cycle witnessing the intervention of both automated database-guided learning and data integration and knowledge experts validation. As these models become more mature, they are clinically validated by participating centres and clinical researchers, and ultimately made available at the point of care thanks to the physical distribution and computational nature of the MD-Paedigree model-driven infostructure. A detailed highlighting of all legacy assets employed in MD-Paedigree Infostructure is described in Table 1.

Legacy Asset WP Project Partner

Health-e-Child/Sim-e-Child/PCDR Science Gateway and Digital Repository

WP14 EU FP6 Health-e-Child EU FP7 Sim-e-Child OPBG PCDR

MAAT

ATHENA Distributed Processing (ADP) engine

WP14 EU FP6 DILIGENT EU FP6 Health-e-Child

ATHENA

Data Curation and Validation (DCV) tool

WP15 EU FP6 Health-e-Child ATHENA

madIS complex data analysis WP15 ECP FP6 TEL+ ATHENA Kreshmoi images search interface

WP15 EU FP7 KRESHMOI HES-SO

PAROS Personalisation Interface

WP16 EU FP7 CHESS ATHENA

AITION KDD and DSS interface WP16 EU FP6 Health-e-Child ATHENA CaseReasoner DSS interface WP16 EU FP6 Health-e-Child SAG Clinical Trial design WP16 IMI EHR4CR HES-SO

Table 1: MD-Paedigree IT Legacy Assets

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Figure 13 synthetically highlights the intended multiple re-use of results from past and ongoing VPH projects:

Figure 13: Intended multiple re-use of results from past and ongoing VPH projects

B.1.3 S/T Methodology and Associated Work Plan

B.1.3.1 Overall strategy and general description

MD-Paedigree has been designed according to the following principles:

• To maximize the societal impact by addressing topics of high scientific significance in 6 disease areas in paediatrics, where the specific modelling and simulation areas have been identified with the aim of benefitting the most from the prior work pursued within former EU-funded projects (Health-e-Child and Sim-e-Child), and selecting issues where reliable modelling is already available and can be made more robust and more reusable by completing its multi-scale integration and performing gap analysis to uncover scientific and technological needs allowing to drive progress beyond the current state-of-the-art.

• To maximize the clinical impact by:

o advancing the state-of-the-art in paediatric patient-specific computational modelling,

o materializing improved disease understanding and therapy outcomes into both clinical routine and translational research,

o validating sustainable models and simulations,

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o making them readily available at the points of care and to researchers,

o providing newly-defined workflows for personalised predictive medicine,

o developing an advanced digital repository which can vertically integrate multi-scale data of incoming patients in the participating hospitals, allowing for similarity search and outcome analysis among patient cases and making models and simulations readily available to clinicians at the points of care and to researchers.

• To establish ambitious project objectives, in line with the Objective ICT-2011.5.2. of FP7-ICT-2011-9, that will fill in the operational gaps between VPH research and clinical practice.

• To identify leading European partners and projects whose knowledge, competences, technology and collaboration will help us exceed the MD-Paedigree project goals.

• To derive an efficient workplan focused on integrated efforts, open standards, compatibility with the ongoing VPH progress.

• To define clear tasks and precise deliverables, supported by risk management analysis.

•

B.1.3.2 Components and their Interdependencies

Figure 14: Components and their interdependencies

B.1.3.3 MD-Paedigree’s main objectives and related milestones MD-Paedigree’s main objectives, briefly described in Section B1.1 and specified in Sections B1.2, B1.3 and in the relevant Work Packages, can be summarized showing how they will be achieved passing through various milestones implying the completion of a series of steps and phases, as highlighted in the Work Plan detailed in the following section. A specification of the timing and means of verification of each partial result leading to the achievement of the objectives will be detailed in the Self-assessment

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Plan due by month 6 (Deliverable 1.3).

1. Patient-specific computer-based predictive models of Cardiomyopathies

The objective is to capture the main features of the cardiovascular system, including the heart, arteries and peripheral circulation, to predict cardiomyopathy progression and plan therapies like heart transplant and ventricular assist devices. By merging all scattered information obtained from different diagnostic tools in clinical practice, and obtaining a generative model of heart function in children, our model will provide cardiologists the tools to deliver patients the best possible medical care. Timing: within the first 36 month, data from 180 patients are collected and re-evaluated (in accordance to Milestones 2 and 3); the main components of the model are going to be completed starting from month 4 until the very end, on month 48 (M2, 3, 4); from month 22 starts the development of Whole-heart Coupled Fluid-Structure-Interaction Simulation (M3, 4). The final model is due to be ready by the end of the project on month 48 (M4).

2. Patient-specific computer-based predictive models of Cardiovascular Disease Risk in Obese

children and adolescence

MD-Paedigree will integrate the variety of known biomarkers for CVD risk assessment into one common framework, enhance body fat distribution biomarker measurement, and analyse interdependencies between the biomarkers. In addition, MD-Paedigree will develop computational models with high predictive power to better understand the mechanism of CVD development. These models will also allow the simulation of interventions to make personalised predictions for the optimal therapy. Timing: within the first 36 month, data from 180 patients are collected and re-evaluated (M2, 3). Starting from month 4 and until month 36, the heart model developed for Cardiomyopathies will be adapted to the obese heart while the Automated assessment of body fat distribution from MRI and ultrasound data will be finalised (M2, 3, 4). From month 12 to month 48 Multi-scale data integration and virtual phenotype generation and Cardiovascular risk stratification and predictive disease and therapy modelling will be developed (M2, 3, 4). The complete and refined model will be available on month 48 (M4).

3. Patient-specific computer-based predictive models of Juvenile Idiopathic Arthritis

Comprehensive and accurate computer models derived from patient-specific data across multiple scales covering body, organs, tissues, and molecular levels will be developed. This data is gathered and stored in a standardised manner building upon the Health-e-Child software tools developed for wrist analysis in the context of JIA. These tools are extended for the purpose of integrating model information related to a wider range of joints, covering morphology, gait analysis, bio/genetic data. The tools to be developed will also include the aspect of a multidimensional longitudinal analysis that yields the opportunity to identify potential new outcome measures (imaging or biological biomarkers) for the assessment of treatment efficacy. Timing: within the first 34 months (until December 2015), data from 180 patients are collected and re-evaluated (M2, 3, 4). Starting from month 4 until month 48, Patient-specific anatomical modelling based on image data will be performed. Automatic biomarker extraction will be performed starting from month 7 to month 42 (M2, 3, 4). Biomechanical simulation based on image-based modelling and gait analysis will start on month 13 and will be finalised by month 42(M2, 3, 4). Multidimensional modelling of disease course will start on month 4 and will be finalised by month 42 (M2, 3, 4). The final model will be ready by month 42 M(4).

4. Patient-specific computer-based predictive models of NND

Accurate personalised biophysical model for patients are driven by clinical practice needs to estimate muscle forces and joint loads in the gait of NND populations, and this requires extracting subject-specific bone and muscle anatomy from MRI images, developing novel, accurate scaling methods for musculo-skeletal modelling, adapting the existing musculoskeletal model to subject-specific and pathology-specific data and designing models driven by the dynamics of gait perturbations. Timing: staring on month 1 and until month 36, Gait analysis collection for Cerebral Palsy will be performed. Gait analysis collection for DMD and SMA will start on month 12 and will be finalised by month 48 (M2, 3, 4). Image acquisition activities will be performed starting from month 3 and will be finalised by Month 36 (M2, 3). The construction of a scalable mass distribution model suitable for the

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paediatric population will start on month 1 and will end on month 48 (M2, 3, 4). The development of a personalised disease specific skeletal model will be performed starting from month 12 and will be finalised by month 48 (M2, 3, 4). The construction of a disease specific muscle model will start on month 1 and will be finalised by month 36 (M2, 3). The design of models driven by the dynamics of gait perturbations will be start on month 12 and will be finalised by month 36 (M2, 3). The complete and refined model will be available at the end of the project, on month 48 (M4).

5. Newly-defined workflows for personalised predictive medicine at the point of care

New clinical workflows will describe the sequence of operations that start with clinical data acquisition and, by using MD-Paedigree’s models, ends with a clinically useful diagnostic index and treatment strategy. The clinical workflow will be subdivided into 4 specific steps: a) acquisition of clinical, structural and functional information, b) integration of all information into a single model, c) similarity search through the digital repository, and d) personalised prediction of disease outcome and optimisation of individualised therapy. Timing:the definition of new clinical workflows will start on month 13 and will end by month 48 (M2, 3, 4). 6. Innovative model-driven Infostructure powered by an established digital repository solution able to integrate multimodal health data. MD-Paedigree will implement the Service-Oriented Knowledge Utility (SOKU) vision, to facilitate the design and development of innovative new predictive models as reusable and adaptable workflows of data mining applications, and turning the latter into clinically validated decision support tools, made available at the point of care to physicians. The MD-Paedigree system consists of standard services that have various levels of knowledge awareness, starting from basic utilities under the form of classical Web services wrapping up computational resources, to hybrid services that only consume data and information, and to finally more complex high-level entities, which produce knowledge. MD-Paedigree exploits recent ground-breaking European research on semantic modelling, ontology-based data access and scalable query execution to develop an extensible platform based on open standards and protocols to deliver a complete and generic solution able to tackle the targeted paediatric disease areas, while remaining adaptable and evolvable to additional disorders in the future. Timing: the MD-Paedigree Infostructure, integrated with GPU services, semantic data representation, information access tools, and data analysis and knowledge discovery tools, will be completed by month 48 (M2, 3, 4).

B.1.3.4 Timing of work packages and their components

The MD-Paedigree project partners have formalized a work plan implementing 4 major phases, implying a number of conceptual steps, over 48 months of activity with 4 major milestones. The first milestone is due after 9 months and marks the end of the specification phase; the following milestones are aligned with the reporting periods of the project every 12 months.

Phase 1 (running from month 1 to 9) – Project Set-up, Requirements Elicitation, and Clinical Protocols: During Phase 1 quality assurance guidelines and a self-assessment plan will be prepared, ethical approval will be obtained , and the first dissemination activities will be performed (Step 1) Furthermore, clinical protocols for the selected paediatric applications will be established (Step 2). Finally, the requirements for models and infostructure implementation will be analysed and documented from an end user standpoint (Step 3).

Phase 2 (running from month 10 to 24) – Baseline Data Collection, Initial Prototypes, First Evaluation and Requirements Refinement: Patient enrolment will take place and data acquisition will be started (Step 4). Based on the established requirements, the existing models from Health-e Child and Sim-e-Child projects will be refined and adjusted to the new applications. The open repository for project infrastructure will be introduced and initialised with the current models and data (Step 5). First evaluations will be undertaken and requirements will be refined based on the collected experience;

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Figure 15 Conceptual flow-chart of phases and steps fulfilling the project’s milestones

additionally, during this phase, the Strategic Exploitation Seminar will be held and the 1st Exploitation Plan will be drafted (Step 6).

Phase 3 (running from month 25 to 36) – Follow-up Data Collection, Advanced Prototypes, Evaluation and Requirements Update: Follow-up or additional data will be acquired for all clinical applications (Step 7). The respective models will be enhanced to process longitudinal data and refined according to the obtained evaluation results. New functionalities will be integrated into advanced prototypes. The open repository will be improved and updated with content (Step 8). A second set of evaluations will be conducted and requirements will be adjusted for the final system. Furthermore, the 1st Training Event will be held (Step 9).

Phase 4 (running from month 37 to 48) – Final Data Collection and Prototypes, Clinical Validation, and Deployment: In the final year, data collection will be concluded and the clinical validation will take place with the final models and simulation framework (Step 10). Results will be used to propose and disseminate improved clinical workflows. Subsequently, the 2nd Training Event will be held (Step 11). Models for all clinical applications and their respective evaluations will be documented and disseminated, while the implementation plan will be refined and the Health Technology Assessment and the Medical Clearance preparatory activities will be performed (Step 12). Figure 15 shows the interconnection of phases, steps, and milestones, while the interconnection of milestones and deliverables is shown in Table 2.

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Milestone Deliverable

M1: Project Set-up, Requirements Elicitation, and Clinical Protocols

Kick-off meeting (D1.1.);Project presentation (D.1.2);Self-assessment plan (D1.3), Quality assurance guidelines (D1.4), Initial requirements analysis (D2.1, D13.1, 14.1), Study protocols and ethical clearance (D3.1, D4.1, D5.1, D6.1, D6.2, D7.1) Dissemination material (D18.1)

M2: Baseline Data Collection, Initial Prototypes, First Evaluation and Requirements Refinement

Patient Enrolment and Data Collection (D3.2, D4.2, D5.2) Modelling Reports, Prototypes and Infostructure implementation(D8.1, D8.2, D9.1, D9.2, D10.1, D11.1, 13.2, 13.3,D14.2, D15.1, D15.2) Evaluation and revised requirements (D12.1, D2.2) Updated Dissemination material (D18.2) Exploitation (D19.1,D19.2, D19.3)

M3: Follow-up Data Collection, Advanced Prototypes, Evaluation and Requirements Update

Data Collection (D3.3, D4.3, D5.3, D6.3, D7.2, D7.3, 7.4), Modelling Reports and Prototypes (D8.3, D8.4, D9.3, D10.3, D10.4, D11.2, D11.3, D14.3, D16.1, 16.2), Evaluation and requirements update (D2.3, D13.4, D18.2) Training event (18.3) Clinical impact assessment scenario (19.4)

M4: Final Data Collection and Prototypes, Clinical Validation, and Deployment

Final Data Collection (D5.4, D6.4); Final Modelling Reports and Prototypes (D8.5, D9.4, D10.2, D10.5, D11.4, D11.2, D11.4, D15.3); Clinical Validation (D12.2, D12.3)

Final reports and Deployment (D1.7, D14.4, D16.3, D18.5, D18.7, 18.8, D19.6, D19.7) Scenario analysis session (D.18.4.1, D.18.2 ); Training event (D18.6)

WP1 Coordination & Project Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T1.1 Monitoring & Scheduling T1.2 Quality & Reporting T1.3 Financial Coordination T1.4 Contractual Management T1.5 Conflict Resolution T1.6 Meetings and Communications Management T1.7 Clustering and Concertation T1.8 Recruiting of Independent Committee Members T1.9 Risk ManagementT1.10 Ethical Clearance and Monitoring WP2 Clinical and technical user requirements for disease modelling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T2.1 Requirement elicitation and documentationT2.2 Requirement revision and management WP3 Data acquisition and processing for Cardiomyopathies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T3.1 Informed Consent & Data Collection Protocol T3.2 Clinical data & Routine laboratory test data collection T3.3 Estimation of functional class and cardiopulmonary test T 3.4 Imaging Acquisition and data processing T3.5 Data upload and integration into the infostructure WP4 Data acquisition and processing for the estimation of CVD risk in obese children 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T4.1 Informed consent & data collection protocol T4.2 Clinical data & Routine laboratory test data collection T 4.3 Estimation of adipokines, low-grade inflammation and insulinT 4.4 Image acquisition, clinical annotation and data processing T4.5 Systolic and diastolic markers of cardiac dysfunction of US and CMRT4.6 Measurement of the blood flow to the gut T4.7 Dynamic study T4.8 Data Upload and Integration into the Infostructure WP5 Data acquisition and processing for Juvenile Idiopathic Arthritis 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T5.1 Data collection protocols and informed consent T5.2 Clinical data collection T5.3 Routine laboratory tests T5.4 Synovial and blood Cytokine and inflammatory mediators profileT5.5 Meta-genomic data analysis (Microbiote) T5.6 Image acquisition and clinical annotation T5.7 Gait cycle analysisT5.8 Data Upload and Integration into the Infostructure WP6 Data acquisition and processing for NND 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T6.1 QA on data collection and clinical protocols T6.2 Gait analysis collection for CP T6.3 Gait analysis collection for DMD and CMT T6.4 Image acquisition T6.5 Data Upload and Integration into the Infostructure WP7 Genetic and metagenomic analytics 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T7.1 Informed consent and data collection protocol T7.2 Sample collection, storage and DNA extraction T7.2.1 Cardiology T7.2.2 Rheumatology T7.2.3 Cardiovascular risk in obesity T7.3 DNA analysis T7.3.1 Cardiology T7.3.2 Rheumatology T7.3.3 Cardiovascular risk in obesity T7.3.4 Gut microbiota analysisT7.4 Data Upload and Integration into the Infostructure WP8 Modelling and simulation for Cardiomyopathies 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T8.1 Personalised anatomical and structural heart T8.2 Electrophysiological and biomechanical modelling and simulation T8.3 Hemodynamic modelling and simulation T8.4 Whole-heart coupled Fluid-Structure-Interaction simulation T8.5 Statistical shape, flow and physiological properties modelling and analysisWP9 Modelling cardiovascular risk in the obese child and adolescent 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T9.1 Heart model adaptation to the obese heart T9.2 Automated assessment of body fat distribution from MRI dataT9.3 Multi-scale data integration and virtual phenotype generationT9.4 Cardiovascular risk stratification and predictive disease & therapy modelling WP10 Modelling and simulation for JIA 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T10.1 Patient-specific anatomical modelling based on image data T10.2 Automatic biomarker extraction T10.3 Biomechanical simulation based on image based modelling and gait analysisT10.4 Multidimensional modelling of disease course

Table 2 Milestones and deliverables

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Figure 16 Executive Gantt Chart

B.1.3.5 Performance/research indicators

MD-Paedigree’s performance/research indicators, through which to assess the progress and impact of the project, are going to correspond to different levels of assessment.

On one hand, the timely delivery of all planned deliverables is the first indicator of the fulfillment of each phase in the expected progress of MD-Paedigree, monitoring what can be demonstrable at each corresponding milestone of the project.

A second and much more detailed means of verification is provided by the assessment criteria for each milestone and each WP which are to be defined in D1.3 Self-assessment plan within month 6.

A third and more traditional level of analysis, with regard to the scientific impact and quality of the expected outcomes, is measured through indicators such as the number of scientific publications and of citations in publications authored by others; while, with regard to overall productivity, the number of patents, or the final stemming of ad hoc spin off companies, are usually taken as success indicators of a RTD project.

MD-Paedigree deems that the definition of an appropriate set of multiple indicators is a complex activity in need of sufficient pondering for being specified with significant accuracy. Such a detailed definition cannot therefore be adequately performed at this preliminary stage, but needs to be postponed until the specific deliverable (D1.3: Self-Assessment Plan) dealing with this issue will be completed. D1.3 is due by month 6.

B.1.3.6 Risk Analysis & Mitigation Plan

MD-Paedigree will build on a risk management system that has been successfully tested in both the

WP11 Modelling and simulation for NND 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T11.1 Construction of a scalable mass distribution model suitable for the paediatric T11.2 Development of a personalised disease specific skeletal model T11.3 Construction of a disease specific muscle modelT11.4 Design of models driven by the dynamics of gait perturbationsWP12 Models validation, outcome analysis and clinical workflows 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T12.1 Clinical assessment and validation T12.2 Integrated clinical workflows and personalised treatment models WP13 Requirements and Compliance for the MD-Paedigree Infostructure 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T13.1 Requirement elicitation and documentation T13.2 Requirement revision and management T13.3 Compliance with VPH-Share T13.4 Data policy definition and implementation WP14 Grid-Cloud Services Provision and GPU Services Integration 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T14.1 Adaptation and Extension of Sim-e-Child Platform T14.2 Open Cloud-API and GPU Integration T14.3 Athena Distributed Processing (ADP) Engine Integration T14. 4 SOKU ImplementationT14.5 Privacy and Security Issues WP15 Semantic Data Representation and Information access 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T15.1 Data curation and validation tool T15.2 Semantic data representation and interoperability T15.3 Case-based querying T15.4 Multimodal case-based retrieval and query reformulation T15.5 Data Modelling and SupportWP16 Biomedical Knowledge Discovery & Simulation for Model-guided Personalised 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T16.1 General data analysis and knowledge discovery toolsT16.2 PAROS Personalisation Platform T16.3 AITION Knowledge Discovery & Simulation Framework T16.4 Data-driven drug and trial design WP17 Testing and validation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T 17.1 MD-Paedigree Infrastructure testing and validationT 17.2 Case- and ontology-based retrieval service testing and validationT 17.3 Beta Prototype of KDD & Simulation Platform testing and validationWP18 Dissemination &Training 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T18.1 Project Web-siteT18.2 Dissemination Materials18.3 TrainingT18.4 Seminars, Workshops, Concertation Activities with Other ICT Funded Projects, and T18.5 NewsletterT18.6 Community Liaison and FeedbackT18.7 Engaging Parent and Patient AssociationsWP19 Exploitation, HTA, and Medical Device Conformity 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48T19.1 Evaluation approach and meaningful indicator development T19.2 Benefit-cost evaluation of MD-Paedigree infostructure T19.3 Benefit-cost scenario for clinical impact assessment T19.4 Exploitation and business planning T19.5 Preparing market access and medical device conformity assessment procedures

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Health-e-Child and Sim-e-Child projects. The risks that may potentially affect the project will be continuously monitored in order to elaborate the corresponding contingency plans. The project coordinator and the project manager will specifically address risk issues at each project management meeting. The risk management tasks consist of risk identification, estimation, mitigation and follow-up.

Risk Identification. All project partners are concerned with risk detection. When a risk is detected, it is reported to the WP leader concerned, who assesses the risk. Risks that are serious, affecting the critical path of the project, are further reported to the project coordinator. Potential risk identification is made at the beginning of the project and allows the identification of some risks threatening the achievement of project goals.

Risk Estimation. The risk estimation is a two dimensional process, focusing on measuring the risk likelihood and the risk impact on the project. The risks are estimated using a numeric scale from 1 to 3, where 3 represents a risk that is almost certain on the likelihood scale, or a risk that is very serious, affecting the critical path of the project, on the risk impact scale.

Risk Mitigation and Follow-up. Each identified risk shall have an owner who is responsible for its mitigation, monitoring and reporting. In addition, the risk owner proposes a preventive and corrective treatment, consisting of suitable actions to reduce the severity and the probability of occurrence of the risk.

A preliminary list of potential risks is presented below. The results from this analysis will be monitored and updated during the overall lifetime of the project (task T1.9: Risk Management).

Risks related to data privacy, security, legal, and regulatory requirements. The requirements related to data privacy and security must be reconciled with applicable legislation. Therefore task T1.10 “Ethical Clearance and Monitoring” have been introduced in the WP1 to address this issue early in the project.

A review at M24 has been demeed necessary, both to check the emergence of new risks and to assess the validity of the risk assessment initially performed. Overall, three new risks have been identified and assessed, while the assessment of the ones already indentified at the beginning of the project has been adjourned.

Risk Rating Discussion and counter measures

Loss of patient data privacy

Low This risk is considered low since it will be guaranteed that only anonymised patient data will be stored and shared. This includes medical images whose meta data (so called DICOM header) will be properly anonymised. Also, it will be guaranteed that only authorized persons will be able to access patient data.

Update at M24: this risk became even lower, since P17MAAT has made available an automatic anonymizer tool to all the clinical partners, to facilitate the data uploading in the Infostructure while minimizing the risk of loss of data privacy. Furthermore, specific guidelines for data management have been provided to the clinical partners.

Loss of patient data security

Low

This risk is handled by partner MAAT France who was responsible for handling data security in Health-e-Child and successfully passed an external security audit. Due to the existing experience and high security standards realized in the Health-e-Child and Sim-e-Child platforms this risk is considered low.

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Update at M24: this risk is still considered low. Progress in the implementation of the Infostructure, integrating state-of-the-art security frameworks, make it possible to reduce this risk.

Delays due to late ethical approval

Medium There is a risk that the start of patient enrolment is delayed due to complex ethical approval procedures. In order to prevent this risk the clinical partners will start preparing the necessary documents foe their ethics committees already during negotiation phase.

This risk has been underestimated at the beginning of the project, but is now considered medium, even taking into account the re-submission of protocols including changes in the enrollment and in the disease involved in the project (CmT1 instead of SMA). At M24 all ethical approvals had been obtained in all clinical centres.

Management Risk


Consortium heterogeneity

Low The project brings together clinicians and scientists with very diverse expertise and background. The integration of the project team presents a risk that will be constantly monitored. The project coordinator will have a very important role in establishing an open communication channel between the clinical and computer science world. Overall the risk is rated low due to the fact that the consortium has partially been working together in prior research projects, and because of MD-Paedigree’s nature as clinically-led project.

Update at M24: this risk is still considered low. The natural process of team building and a more effective mutual understanding have, normally, the effect of minimizing such risk during the project lifetime.

Underestimation of the required effort

Medium

This risk is handled by the WP leaders monitoring the planned versus actual effort required by each task. Indicators and statistics will be included into periodic progress reports to the project coordinator. Due to the ambitious goals of the project, the risk is rated medium.

Update at M24: the risk is still considered Medium. In fact, the current delay of some parts of the project is due more to unexpected difficulties (i.e. the lack of available patients for some pathologies) rather than an underestimation of the effort required.

Turnover of key personnel

Low This risk is managed by standardizing the way of working across the various teams and by defining a backup policy, so that in case of unexpected departure, remaining personnel can temporarily compensate for the absent ones, while waiting for a permanent replacement. Since all but 2 partners can resort to numerous permanent staff, the risk is rated low.

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Update at M24: this risk is still rated low. Indeed, the only circumstance which has created a serious issue has been the premature demise of our esteemed colleague Alexey Tsymbal, from SIEMENS AG. This disgrace has temporarily hindered the prosecution of WP9, which has been eventually taken over by Olivier Ecabert.

Insufficient participation of the communities represented to the public review process.

Low Progressively intensify the dissemination activity stimulating the

participation through the use of tools like discussion forums and mailing

lists among others.

Update at M24: this risk remains low.

Technical Risk


Diversity of medical procedures and complexity of problem domain

Medium This risk is handles by frequent multidisciplinary discussions. Because clinical and technical experts will interact closely and on regular time points, it is not very likely that major problems will remain with respect to the diversity of medical procedures and complexity of the domain. For gait analysis some differences exists, but not too different. An initial consensus effort is explecetely part of the project.

Update at M24: the process of protocols’ alignment has minimized such risk in general. Still, it has been now rated as medium, since – with particular regard to the cardiomyopathies study – it has emerged that the differences in treatment procedures at DHZB resulted in a minor availability (in that centre) of patients compatible with the chosen inclusion criteria. A refinement of such criteria have minimized the risk of impact of this diversity in medical procedures for the prosecution of the project.

Insufficient

quantity or

quality of the data

Medium

One risk is to start too late with data acquisition. Since the project requires longitudinal data (up to 3 year follow up), it is crucial to start early with data acquisition. Therefore the clinical protocols will be finalized within the first data 3 months of the project and ethical clearance will be prepared already during negotiation phase. Partly the risk is also minimized since part of the data is already available from Health-e-Child and Sim-e-Child. Another risk is that some very specific data won’t be available in sufficient quality or quantity. Since all the clinical centres are equipped with the latest generation of equipment and perform hundreds of imaging procedures and interventions every year, this risk is rated low.

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Update at M24: this risk has been underestimated at the beginning of the project. In fact, delay in ethical approval, the full operability of new MRI machines, the limited amount of machine-time available for the project, have all hindered a fully satisfactory data collection process. Still, appropriate mitigation strategies, as well as the introduction of new tasks specifically devoted to data management, have minimized the potential negative impact of this risk in the project prosecution.

Developed models are incomplete or not usable

Low The risk of final non-acceptability and non-usability of the developed models has been implicitly considered from the very beginning of the project. Thus, it has been deemed useful to add it to the risk table. Still, this risk has been rated as low, given the role which the ongoing validation process is expected to play, and its inherent risk- minimizing effect, ensuring that clinicians assess the concrete usability of the models and help to fine tune their UI.

Developed platform is not usable

Medium This risk takes into account the possibility that, even if the implementation of the core features of the platform will be completed on time, it will still be possible that there might be insufficient time to make fullyusable (and user-friendly to non-expert clinical end-users) all the developed tools. To minimise this risk, the Agile Project Management approach has been introduced for the platform implementation. This will facilitate better and more effectively taking into account the users’ needs, requirements and expectations, continuously gathering feedback from the end-users to fine-tune the platform. Training activities, testing and Scenario Analyses Sessions have been subsequently adjusted to contribute to the Agile Methodology. Still, this risk is rated as medium, mainly due to the time constraint.

Lack of clinical validation of the models

Medium This risk has been introduced to take into account the complexity of the foreseen validation methodology, as per D12.1, as well as the possible delay in the complete implementation of the models, due to further delays in the data collection. Such delays could prevent the timely finalization of the models, thus affecting the subsequent validation process. The risk is rated as medium, even though the subsequent validation phases are likely to be performed in a smoother way, thanks to the cooperative effort alrady put in place during the second reporting period.

Table 3: Risks rating analysis and counter measures

B.2 Implementation

B.2.1 Management Structure and Procedures

The MD-Paedigree project management, organised according to the structure illustrated in Figure 16, focuses on four primary managerial tasks:

• Decision Making, implemented by the Governing Board,

• Scientific and Technical Coordination, performed by the Management & Technical Coordination Board,

• Operational Management, performed by the Management & Technical Coordination Board,

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• Advisory, carried out through four bodies, which will be largely composed of personalities selected among experts holding external positions with regard to MD-Paedigree partners:

• the Scientific Committee,

• the Ethical and Legal Committee,

• the Interoperability Steering Committee,

• the Users’ Board.

The arrangement structure builds partially upon the experience and the successful working practice which have been accrued in particular in managing the Health-e-Child and Sim-e-Child projects; the innovations introduced are aimed at ensuring an enhanced capacity to convert the results of the project in immediately usable and accessible tools for clinical practice and for a full acceptance by the researchers community, also taking into account the VPH community’s former efforts in the fields both of modelling and of infostructure provision.

The rationale and specific roles of these bodies, and their interaction towards a coherent management structure, have been planned to reflect the nature of the project, with the aim of enabling the achievement of its goals.

The Governing Board is the highest level of management in the MD-Paedigree project. It is the Consortium’s main decision-making and arbitration body.

P1 - OPBG is MD-Paedigree’s Coordinator and the Governing Board is chaired by Prof. Bruno Dallapiccola, Scientific Director of OPBG, with a proven track record in directing large heterogeneous research bodies.

One representative from each of the other Consortium Partners will participate in the Governing Board. This body will make all definitive decisions for the Project. Convening on a yearly basis, but summoned if urgency requires, this body will be appropriately instructed and called upon to express the participating organisations’ position on important issues. Each Member and the Chairman has one vote, and a relative majority system will be employed, defined in an appropriate Consortium Agreement. In particular, the Governing Board has the following functions:

• Approve the allocation of the project’s budget;

• Request contractual changes to the European Commission;

• Approve any changes in the Consortium Agreement;

• Examine liability for default situations;

• Vote on termination of a partner according to rules of the Consortium Agreement;

• Approve new partners or legal status changes to existing partners;

• Review the progress of the work programme as a whole;

• Approve requests for changes proposed to the description of work;

• Approve proposed strategic project guidelines;

• Emit guidelines regarding external communication;

• Propose and approve resolutions of critical issues and conflicts;

• Appoint the Advisory Committees.

The Management & Technical Coordination Board ensures both the project managing and the technical and scientific coordination of the project, and is chaired by the Project Coordinator, who is supported by Dr. Sonya Martin, Head of OPBG’s Grant and Technology Transfer Office with 10 years of experience in project management, and by the MD-Paedigree Project Manager, Prof. Edwin Morley-Fletcher, President of Lynkeus. The latter has a proven experience in handling complex technical and scientific projects, having worked as Project Manager within the FP6 IP Health-e-Child and the FP7

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STREP Sim-e-Child. Furthermore, the Management & Technical Coordination Board comprises also the Leaders of A1 (Prof. Giacomo Pongiglione, who was already clinical coordinator in Health-e-Child and Sim-e-Child), A2 (Dr. Michael Sühling, Leader of the Biomedical Informatics Research Program within the Image Analytics and Informatics Global Technology Field in Siemens CT , who has been the Coordinator of Sim-e-Child), and A3 (David Manset, CEO of Maat, winner of the ICT08 First Prize for the Health-e-Child platform). The Management & Technical Coordination Board meets regularly by telephone conference on a weekly basis, inviting to attend, on rotation, also those, among the Work Packages’ Leaders, who are relevant to the Action in focus.

The clinical leadership in an ICT for Health project is a specific organisational innovation pursued by MD-Paedigree, in the belief that a clinically-led governance ensures more speedily and efficiently the real involvement of the hospitals in the implementation both of the models and of the infostructure, in order to achieve, in the course of the project, the needed acceptability and hit the ambitious target of direct usability in routine clinical workflows.

The primary responsibilities of the Project Coordinator are:

• To be responsible for the Community financial contribution regarding its allocation between partners and activities, in accordance with this grant agreement and the decisions taken by the consortium. The coordinator shall ensure that all the appropriate payments are made to the other partners without unjustified delay.

• To keep the records and financial accounts making it possible to determine at any time what portion of the Community financial contribution has been paid to each partner for the purposes of the project.

• To inform the Commission about the distribution of the Community financial contribution and the date of transfers to the partners, when required by the grant agreement or by the Commission.

• To perform the final review of the reports to verify consistency with the project tasks before transmitting them to the Commission.

• To monitor the compliance by partners with their obligations under the grant agreement.

• To oversee the legal, ethical, financial and administrative management including, for each of the partners, the obtaining of the certificates on the financial statements and on the methodology and costs relating to financial audits and technical reviews.

• To identify, assess, and mitigate risks.

With regard to project management within the Management & Technical Coordination Board, this activity, apart from the tasks which cannot be delegated by the Coordinator, will be performed by the Project Manager, Prof. Edwin Morley-Fletcher, under the supervision of the Coordinator.

Prof. Morley-Fletcher will support the Project Coordinator in running WP1 and ensuring that the project is on-time, on-objectives, and on-budget, monitoring the planned progress of the activities in the various work packages.

Besides providing a global support action to the Coordinator, he will be specifically performing the following tasks:

• To ensure the timely preparation and drafting of the reporting documents, which will be supervised and eventually submitted by the Coordinator to the European Commission;

• To maintain an appropriate project communication infrastructure, including website and document repository;

• To set-up the project’s online management platform and train the partners to use it;

• To organise project meetings;

• To interface with general external requests for information.

Such organization of the Management and Coordination roles has proven to work functionally in the preceding FP6 Health-e-Child and the FP7 Sim-e-Child projects. A key benefit to this management

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structure is the familiarity that key partners of MD-Paedigree already have with such a mechanism and the understanding of each partner’s roles and remits. Furthermore, the independence of the Project Manager has proven to be an asset at dealing with possible internal conflicts, while not diminishing the Project Coordinator’s full assumption of all his responsibilities as required by the ECGA.

The Scientific Committee guarantees expert technical counsel to the Management and Coordination Board in dealing with the scientific orientation, scientific progress, and scientific challenges of the project. It is chaired by Dorin Comaniciu Ph.D., Global Technology Head for Image Analytics and Informatics at SCR, who has already been performing in the same responsibility within Health-e-Child and Sim-e-Child and is composed of senior professors and scientists recruited by the Management and Coordination Board from inside and outside the Consortium Partners. In particular, the Scientific Committee will have the following functions:

• To evaluate the scientific content of the planned activities and propose changes to improve technical and scientific excellence;

• To assess technical progress by comparing the project results to the state-of-the-art;

• To periodically organise sessions for auditing and evaluating the research performed;

• To stipulate and evaluate measurable results for project activities;

• To monitor technical quality of publications;

• To oversee major experiments and testing.

The Ethical & Legal Committee ensures the ethical clearance of all the project’s activities and their adherence to the relevant European regulation. The Committee will be chaired by an independent personality, Monica Lopez Barahona, biologist, dean of the Science Faculty and member of the Ethical Committee of the Francisco de Vitoria University in Madrid, Spain.

The Ethical & Legal Committee comprises representatives of the clinical partners, a member of the Scientific Committee and a representative of the Management and Technical Coordination Board, as well as internationally recognized experts in bioethics.

In particular, the Ethical and Legal Review Committee has the following functions:

• To monitor the process of seeking local Ethical Committees clearance;

• To examine the yearly Work Plan for ethical or legal questions and approve release;

• To monitor and review project deliverables authorizing release where ethical questions arise;

• To monitor for upcoming ethical and legal implications;

• To propose solutions to legal and ethical questions coming from the work package leaders.

The Interoperability Steering Committee monitors the provision of an ongoing specific interoperability support for a coordinated connection with other EC funded projects, and in particular with open-source VPH repositories, to ensure the continuity of the scientific and technical efforts. It is chaired by Prof. Rod Hose, coordinator of VPH-Share.

The Users Board highlights the external stakeholders point of view on the outcomes of both the modelling and the infostructure development, in order to assess the degree of ongoing acceptance of MD-Paedigree results by the users community. It is chaired by Prof. Marco Viceconti, chairman of the VPH Institute.

For the purposes of project management and work package organisation, the work plan of MD-Paedigree has been logically split into four cognate Activities, as already explained in paragraph 1.3.2 “Work Plan”. These activities enable separation of responsibilities across project partners and isolation of project milestones and deliverables for reporting purposes.

In Figure 17 the activities are listed across the following four groupings:

A1. Clinical Background Activities, User Requirements, Validation, Outcome Analysis, Workflows;

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A2. Modelling and Simulation;

A3. MD-Paedigree Infostructure;

A4. Coordination Activities.

Every activity has a specific leader that guarantees the control of the progress in each WP, ensures the coordination between the WPs and the interoperability between data and knowledge progressively emerging during the project, faces eventual criticalities and performs a periodical report and revision of the progress.

Figure 17 – Governance Structure

(ATHENA)

Person in charge: Y. Ioannidis (ATHENA)

(VUmc)

Person in charge: O. Ecabert – ad interim (SAG)

Led by Olivier Ecabert (SAG)

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B.2.2 Beneficiaries

PARTNER 1: OPBG Ospedale Pediatrico Bambino Gesù – Private Research Institution –

Italy

Organisation description

General description: The Bambino Gesù Paediatric Hospital (OPBG) is a health care and research institution, specialised in paediatric and developing ages, part of the Italian National Healthcare System and widely recognized as referral centre for all paediatric specialties at national and international level. Thanks to its organization, structures, technologies and highly qualified health care professionals, it guarantees total coverage for all health care needs, including emergencies.

Expertise: OPBG’s Research Areas are: immunology, oncology-haematology, genetics and rare diseases, multifactorial diseases and complex phenotypes; clinical-technological innovations; organizational and management models; regenerative medicine; clinical innovations and trials; management innovations; integrated information networks; biobank; technological platform.

Facilities: Specific laboratory research facilities are organized in modern laboratories, fully equipped with high-tech devices for molecular, cellular and animal work supporting post-genomics, proteomics, microarray technology, cell and molecular biology, including animal facilities, cores for flow cytometry and cell sorting, TCR spectratyping analyses, sequencing, statistical and informatics analysis. Instrumentation for molecular and cell biology, tissue culture, microscopy, PCR and biosafety level 2 rooms for vector handling.

Previous participation in EU Funded projects: OPBG was clinical coordinator in two FP6 and FP7 ICT for Health projects: Health-e-Child (2006-2010) and Sim-e-Child (2010-2012). OPBG participated and is currently involved in several other EU funded projects: a) within FP6: NoE EUROPRISE; NoE TREAT-NMD; EUGINDAT; “B Cells linking innate and acquired immunity in mouse and human”; b) within FP7: NeoMero; WoRHD; EURO-PADnet; LEUKOTREAT; PERS; GRIP; CELL-PID; EPICE; InTreALL. ERANET projects: ERANET PRIOMEDCHILD: NEUROGENMRI; ERANET E-RARE: OSTEOPETR (coordinator), EUROBFNS (coordinator), EUROSPA, PodoNet; EuroGeBeta; EuroCGD; within Public Health (DG SANCO): EUROFEVER, EURONEOSTAT I EURONEOSTAT I; TAG; RD PORTAL 2; ORPHANET EUROPE.

Role in the Project: OPBG will coordinate the entire project, assuring scientific coordination and the project management, also by chairing the Management and Coordination Board. OPBG, leader of WP 1, will therefore a) manage the interaction and communications with the EC, trough the project officer; b) be responsible for the allocation and distribution of the EC’s funds among the partners; c) keep records and financial accounts; d) coordinate and be responsible for the preparation of intermediate and final reports; e) monitor the partners’ compliance with their obligations under the grant agreement; f) oversee legal, ethical, financial and administrative aspects of the project; g) identify, asses and mitigate risks. OPBG will also lead A1 on “Clinical background activities, user requirements, validation, outcome analysis, workflows”, coordinating the clinical partners in their activities aimed at patient enrollment and data collection. Furthermore, OPBG will lead WP2 on Clinical and technical user requirements for disease modelling, WP3 on Data acquisition and processing for cardiomyopathies, WP 7 on Genetic and metagenomic analytics, and WP12 on Models validation, outcome analysis and clinical workflows. OPBG will participate into allother clinical WPs, also by contributing to the protocol design and to recruiting patients. . Finally, OPBG will also contribute to the dissemination and exploitation activities by means of conferences, seminars, newletters, etc.

Key personnel

Bruno Dallapiccola, Prof, MD, (M), specialist in medical genetics, Scientific Director of the Ospedale Paediatrico Bambino Gesù. Prof. Dallapiccola is one of the major national expert in

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genetics and in the care and management of patients with rare diseases. He coordinates Orphanet-Italia, represents Italy in the EUCERD consortium, is member of the Scientific Board of many associations of rare disease patients and actively collaborates with the Italian union of rare diseases patients’ associations (UNIAMO). He shall guarantee scientific coordination of the project, through OPBG’s Grant Office, and provide scientific leadership of the clinical aspects.

Sonya Jane Martin, MD, (F), Head of the Grant and Technology Transfer Office. Specialised in healthcare and research management, has over ten years of experience in project management of EU funded projects and IPR-related issues.

Enrico Bertini, MD, PhD, (M), is a scientist and a clinician devoted to study the pathogenesis and treatment of neurodegenerative disorders, particularly neuromuscular disorders and ataxic disorders in childhood. Since 1997 he is the Head of the Laboratory of Molecular Medicine and of the clinical Unit of Neuromuscular and Neurodegenerative disorders at the OPBG and his clinical and research activity is directed to increase knowledge on the genetic bases of neurodegenerative disorders and on mitochondrial encephalopathies.

Enrico Castelli, MD, (M), specialized in neurologic and rehabilitation treatment of children (0- 18 years old) with congenital (cerebral palsy, genetic disorders, spina bifida) and acquired brain and spinal cord injuries (TBI, stroke, post anoxic damage, infectious and tumours). Coordinator of the clinical and research activities of the Laboratory of Movement Analysis and Robotics (MARLab) of the OPBG.

Fabrizio De Benedetti, MD, PhD, (M), Head of the Rheumatology Unit and of the Rheumatology Research Unit, is internationally recognized as a leading researcher in paediatric rheumatology. The main achievement of his translational research has been the identification of the role of interleukin-6 in juvenile idiopathic arthritis (JIA) and particularly in systemic JIA. Dr. De Benedetti is at present the principal investigator of two international global phase III randomized clinical trials. He has published approximately 80 articles in peer-reviewed journals.

Melania Manco, MD, PhD, (F), specialist in endocrinology, researcher in diabetes, obesity and cardiovascular risk in children. Currently PI of a national project funded by the Italian’s Ministry of Health aimed at assessing cardiovascular risk in a large population of overweight children.

Giacomo Pongiglione, Prof, MD, (M), Head of the Department of Cardiology, held in the past the same position within the Istituto Giannina Gaslini in Genoa, and has been the Clinical Coordinator of both the Health-E-Child (FP6) and Sim-e-Child (FP7) EU funded projects since their beginning.

Paolo Tomà, MD, (M), is Head of the Imaging Department, expert in MRI imaging techniques. He led the radiology effort within IGG in the Health-e-Child project.

PARTNER 2: UCL - University College of London – University – United Kingdom


General description: UCL is a large academic university associated with one of the largest paediatric hospitals in Europe – Great Ormond Street Hospital for Children NHS Foundation Trust (GOSH).

Expertise: UCL has one of the largest cardiovascular imaging (echocardiography and MRI) department. The cardiovascular MR department is renowned for its research excellence and forms a crucial part of the UCL Institute of Cardiovascular Science research strategy. The department has a track record of developing novel imaging methods for the assessment of cardiac and vascular function, and has developed novel ways to image and analyse visceral and peripheral fat. Moreover it has been at the forefront of integrating engineering and computer modelling with imaging for the use in the development of safe implantation of cardiovascular devices and more generally, through the FP6 Health-e-Child project, for all treatments of congenital heart disease.

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Other participation to European projects and Networks of reference: Prof. Taylor was previously involved with the Health-e-Child project as a clinical partner. Dr. Diaz is currently the PI of a Marie Curie Initial Training Network (MeDDiCA; www.meddica.eu; FP7 PEOPLE Programme) and the 'Digital Patient' Roadmap project DISCIPULUS ('digital-patient.net'; FP7 VPH).

Role in the Project: UCL will define the intellectual need for why computer modeling may be useful in paediatric cardiology in cardiomyopathies and the obese child. It will be responsible for the acquisition of clinical data, biomarker samples and imaging data for the cardiomyopathy and obese child aspects of the project. UCL will define the data to be acquired. We will acquire ethical and institutional approval for the studies performed at our Institution. The department will develop imaging methodologies to better assess cardiomyopathies and the obese child. UCL will interface between the computer scientists and the clinical data to ensure that the ICT component of the project remains focused on clinical issues that are useful for patient care. Finally it will be responsible for clinical data/ computer modeling research output (presentations, abstracts and publications) and for providing an interface with the public for this project. UCL will also play a role in the training aspects of MD-Paedigreeby organizing 2 events (years 2 and 4).

Key personnel

Andrew Taylor, Prof, (M), UCL Professor of Cardiovascular Imaging ; Head – Centre for Cardiovascular Imaging, UCL Institute of Cardiovascular Sciences; Director – Centre for Cardiovascular MR, Great Ormond Street Hospital for Children NHS Foundation Trust. He has the overall responsibility for the programme of work at UCL/GOSH – project design, administration, and research output. Integration of clinical data into clinical scenarios.

Michael Burch, MD, (M), Head of Cardiology – Great Ormond Street Hospital for Children NHS Foundation Trust; Director – Cardiothoracic Transplantation & Consultant Paediatric Cardiologist. He will be in charged of the supervision of the dilated cardiomyopathy component of the project at Great Ormond Street Hospital for Children

Vivek Muthurangu MD, (M), Head – Cardiovascular MR Research, Great Ormond Street Hospital for Children. He will be responsible of the supervision of the obese child component of the project at Great Ormond Street Hospital for Children. Development of novel MR imaging methodologies.

Vanessa Diaz , PhD (F), Lecturer in Bioengineering at UCL. Bioengineering, technical lead of the 'Multi-Scale Cardiovascular Engineering' group (MUSE), developing applications in computational physiology and training

PARTNER 3: IGG - Istituto Gianna Gaslini – Public Research Institution – Italy


General description: The Istituto Giannina Gaslini (IGG) is the largest public paediatric tertiary care and research hospital in Italy, dedicated to the comprehensive health care of infants, children and adolescents.

Expertise: the unit “Pediatria II - Reumatologia” of the Istituto Giannina Gaslini, which is the only EULAR Centre of Excellence in Rheumatology in Europe for what concern paediatric rheumatology, is the headquarter of the Paediatric Rheumatology International Trials Organization (PRINTO) which is composed of more than 200 rheumatology networks in the world. PRINTO’s main goal is to coordinate, foster and facilitate multi-centers, international controlled clinical trials. The unit has developed most of the currently available instruments to measure, from a clinical as well as from a radiological point of view, disease activity and damage in paediatric rheumatic diseases. IGG has extensive experience in imaging in the paediatric rheumatic diseases and invested a great deal of effort to devise novel imaging methods for the assessment of disease activity and structural damage progression.

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Facilities: Quantitative imaging bio-marker extraction thanks to the collaboration with the Department of Computer and Information Science of the Univeristy of Genoa. Motion and gait analysis laboratory. Spatial and time parameters analysis. Kinematic analysis. Kinetic analysis. Dynamic EMG, Optopletismography Laboratory of Immuonolgy of “Pediatria II - Reumatologia is supplied with cell culture facilities, fully equipped with the necessary instruments: vertical and horizontal laminal flow hoods, CO2 incubator, direct and reverse microscopes, fluorescent microscope, ELISA reader, Four colour cytoflorimetry Confocal microscopy, Quantitative real- time PCR, Mass spectrometer, Immunohistochemestry, Cell sorting The Lab hosts a Biobank were PBMC and sera from more than 600 JIA patients are collected.

Other participation to European projects and Networks of reference: The unit within IGG involved in the project has received several EU Grants over the last few years: EUTRAIN PEOPLE MARIE CURIE ACTIONS, Marie Curie 46108-2008 EUROFEVER 2010 ERANET PRIOMEDCHILD. Furthermore, in previous years most of the partners of this consortium have been interacting in the frame of the above-mentioned EU grant Health- e-Child Integrated Project .

Role in the project: IGG will lead the rheumatology disease-modelling study. IGG will be responsible and the main driver of the WP5. IGG will coordinate data acquisition (clinical data, imaging, in vitro, gait analysis, etc.) that will be used for building the multi-scale integrated disease model, as well as for the anatomical Model Integration and Biomechanical Simulation. IGG will provide clinical expertise and information (clinical reports, scientific literature, etc-.) that will be integrated as prior knowledge into the simulation system. IGG will also participate to the activities of image analysis and information extraction, providing clinical and scientific support to the activities of modelling and simulation. IGG will be also involved in the validation of the developed physiology simulations against real clinical data. As all the other partners, IGG will also take part into the dissemination and exploitation activities.

Key personnel

Alberto Martini, Prof, MD, (M) is Professor of Paediatrics at University of Genova and Head of Pediatria II (G. Gaslini Children Hospital) which is the only EULAR Centre of Excellence in Rheumatology in Europe for what concern paediatric rheumatology. Alberto Martini is moreover President of the Paediatric Rheumatology European Society (PRES), a scientific society for healthcare professionals in the field of paediatric rheumatology, founder and Chairman of PRINTO. Prof Martini main clinical and research interest is represented by paediatric rheumatic diseases. He has published more than 300 papers on international peer reviewed journals including the top ones (N Engl J Med, Lancet, JAMA, Nat Med, J Clin Invest, J Exp Med). Prof. Alberto Martini will be leading the rheumatology disease-modelling study.

Clara Malattia, MD, PhD, (F), Paediatric Rheumatologist. Her research focus is imaging in chronic rheumatic disease of the childhood. She devised the first paediatric-targeted MRI scoring system for JIA. She won the Kourir Award granted to the best research work that could provide new insights into development of treatment of JIA respectively in 2008 and 2009. Her study titled “MRI protocol and score for JIA” was recognized as the best Health-e-Child Clinical Research. She is the author of more than 35 full-papers on international journals. Role in the project: she will coordinate data collection among the Clinical Partners. She will be directly involved in imaging data analysis.

Marco Gattorno, MD, (M), Paediatric Rheumatologist. His main scientific interests involve the study of the pathogenic mechanisms of the rheumatic conditions in children. He is author of more of more than 100 full-papers on international journals.

Paolo Moretti , MD, (M), Director Service of Physical Medicine and Rehabilitation, Istituto Gaslini Children Hospital Genova. Adjoint Professor, Medical School of Specialization in Physical Medicine and Rehabilitation of University of Genova (1998 - to date). National Council member and Regional Secretary of the Italian Society of Physical Medicine and Rehabilitation (2008-to date).

Gian Michele Magnano, MD, (M), is Head of the Imaging Department expert in MRI in the assessment

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of juvenile idiopathic arthritis.

Maura Valle, MD, (F), Paediatric Radiologist. Her research focus is imaging in musculoskeletal disorders.

PARTNER 4: DHZB – Deutsches Herzzentrum Berlin – HOSPITAL – GERMANY


General description: the Deutsches Herzzentrum Berlin (German Heart Institute Berlin, DHZB) is one of Europe’s largest Heart Institutes. The research activities of the DHZB cover almost all emerging fields of cardiac diagnostic and therapy, which is reflected by more than 120 peer reviewed publications annually. Expertise: One major research focus is on non-invasive cardiovascular imaging of patients in all age groups (from infancy to late adulthood). Research of imaging based modelling has been successfully introduced in the past years in is currently applied in several clinical conditions (including heart failure in mitral and aortic valve disease, pulmonary stenosis, aortic coarctation). In addition modelling of blood flow in new valve substitutes is part of the DHZB research (EU FP7 “life valve” project) The imaging science group of the DHZB covers the full spectrum of cardiovascular research that ranges from the development of hard- and software, the conduction of small and large animal research, translational science and the lead in clinical multicentre studies. Other participation to European projects and Networks of reference: The DHZB has close international ties and long standing collaborative projects with some of Europe´s leading research institutes. In addition, the DHZB is an important partner of the German Centre for Cardiovascular Research (DZHK; http://dzhk.de/) that is currently Germany’s largest joint research initiative in cardiovascular medicine. It is part of the core lab for image processing that was initiated by the German Competence Network more than 8 years ago. In this work, the DHZB contributed to build-up the world largest data base of highly standardized CMR imaging datasets from which paediatric reference values will be applied to CARDIOPROOF.

Role in the project: DHZB will contribute to the cardiomyopathy and obesity-modeling aspects of the study by providing integrated clinical data including imaging, genomics, and biochemical markers.

Key personnel

Professor Titus Kuehne (M) He is formally affiliated with the DHZB and the Charité, Medical University Berlin where he has a Professorship for Non-invasive Cardiovascular Imaging. At the German Center for Cardiovascular Research / German Competence Network - CHD he is heading the imaging infrastructure section. He has expertise in imaging of all age groups (from infants to the elderly) and strong international scientific merits concerning the MR imaging modalities (including 4D blood flow) and post-processing framework (CAIPI).

Lucio Biocca (M) and Marcus Kelm (M): physician researchers actively involved in the project.

PARTNER 5: KU-Leuven - Katholieke Universiteit Leuven & University Hospital Leuven –

Public Research Institution – Belgium


General description: KU-Leuven unites the expertise of the Clinical Motion Analysis Laboratory (CMAL), the Cerebral Palsy Reference centre and the Neuromuscular Reference centre for children of the University Hospitals of Leuven with the expertise of two research centres of the University of

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Leuven, namely:

1) the Department of Rehabilitation Sciences

2) the Department of Developmental Regeneration

Expertise: the expertise on clinical gait analysis and related clinical decision making in CP and clinical decision making in NMD is crucial for the success of this research project. Objective evaluation of gait is routinely used for treatment planning based on a full arsenal of treatment modalities. Besides the state-of-the-art facilities of the gait laboratory and the technical know-how, this research group also has extensive experience on conducting, analysing and interpreting the complex dataset of integrated 3D analyses of gait and on the development of new progress in evaluation methods for children with CP. The NMRC Leuven is an internationally recognized centre of excellence for diagnosis, management and research in the area of rare inherited neuromuscular disorders treating over 1,000 patients annually both adults and children

Facilities: the 3D CMAL of the University Hospitals supplies the required high-tech infrastructure for the gait analysis studies of the research centres. The university Hospital has a 3 Tesla MRI.

Other participation to European projects and Networks of reference: KU-Leuven is currently by far the largest university in Belgium in terms of research funding and expenditure, and is a charter member of LERU. Leuven participates in over 350 highly competitive European research projects (FP7, 2007-2013, up to mid 2011), including 34 of the highly prestigious European Research Council grants (ERC), which place the university fourth in the EU. KU-Leuven conducts fundamental and applied research in all academic disciplines with a clear international orientation. Research expenditure exceeded EUR 348 million in 2010. The CMAL is co-applicant of the EU CMASter Erasmus project (lifelong learning). The NMD research group contributes as principal or co-investigator of several clinical international multicentre trials, including registration-directed, pharmacological randomized controlled trials and international multicentre studies on Natural History and Outcome Measures in Duchenne muscular Dystrophy. The NMRC is partner of the TREAT NMD Alliance (working group Care and Management , and Outcome Measures).

Role in the project: The partner KU-Leuven will provide clinical input into the MD - Paedigree project. KU Leuven will recruit (prospectively and retrospectively) participants from large clinical databases on gait analysis and clinical decision making in CP and NMD. The clinical expertise, the gait analysis data and the NMR data will be input for the ICT developer. KU-Leuven will also take part of the dissemination and exploitation activities of the project.

Key personnel

Kaat Desloovere, Prof, PHD (F), is service and Research Manager of the Clinical Motion Analysis Laboratory, at the University Hospital in Pellenberg (Leuven, Belgium). She finalised her PHD in movement sciences in 1996. Since 2003, she is an associate professor at the Department of Rehabilitation Sciences at the Katholieke Universiteit Leuven. Her work focuses on the clinical decision making based on objective gait analysis and objective trunk and upper extremity analysis for children with CP. She will be the coordinator of the NMD activities, supervisor of the gait analysis studies, the MRI data collection in Children with cerebral palsy and neuromuscular disorders, and the development of the clinical database for gait analysis in cerebral palsy.

Guy Molenaers, Prof, MD (M), is a paediatric orthopaedic Surgeon, staff member at the University Hospital of Pellenberg (Leuven) and medical director of the Clinical Motion Analysis Laboratory in Pellenberg. He became the head of the Cerebral Palsy Reference Centre in 2004. In 2003, he received his PhD in Paediatric Orthopaedics. He will be the clinical advisor in the project for patients with cerebral palsy

Nathalie Goemans, MD, (F), is Head of the Neuromuscular Reference Centre for Children and clinical chair of Child Neurology at the University Hospitals Leuven and has longstanding experience in clinical management and clinical research in NMD. She participated in development and dissemination of international guidelines for diagnosis and treatment of NMD ,and is actively involved in the development of study protocols for international multicentre trials. She will be the clinical advisor in

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the project for patients with neuromuscular disorders, and clinical supervisor for the imaging data collection.

PARTNER 6: VUmc - Stichting Vu-VUmc – Public Research Institution – The Netherlands


General description: Stichting Vu-VUmc (VUmc) is a university hospital as well as a medical faculty of the University of Amsterdam. After being runner up for many years, in 2010 VUMC was ranked as #1 in the Netherlands for its research output and quality. The clinical research programme of the department of rehabilitation medicine is part of the MOVE research institute of the VU university. MOVE is one of the world's leading research institutes with respect to human movement research.

Expertise: The scientific areas of the MOVE institute are very broad, within the program "musculoskeletal biomechanics" (a group of 30 people: professors, postdocs and PhD students) both clinical and fundamental projects take place. Expertise includes the analysis of muscle function from animal to normal to pathological human models. Further more advanced techniques for human movement analysis applied to a whole range of applications (spine, upper extremity tasks, gait, etc). Biomechanical analyses include inverse dynamics analysis, to estimate muscle function within the total movement, as well the load on bones and joints. Also the use of ElectroMyoGraphy (EMG) is elaborated as a source of information. The expertise includes both experimental techniques as well as computational biomechanics. For the clinical projects are focused to pathological gait, and aim advanced diagnosis and treatment evaluation. Strong collaboration with physicians and surgeons in VUmc is a key feature of our group.

Facilities: the clinical group has an overground laboratory for gait analysis, a (treadmill) virtual reality gait lab and a muscle function lab with several robotic manipulators and ultrasound. The hospital has a 3 Tesla MRI.

Other participation to European projects and Networks of reference: The laboratory of clinical movement analysis is leading in Europe (its head is president of ESMAC) and principal co-applicant of the EU CMAster project (lifelong learning). The group has shared its ICT developments (human movement analysis software) as freeware and open source to the community.

Role in the project: VUmc dept. rehab. med. will provide clinical input to MD - Paedigree, and will coordinate all activities in the area of the neurological and Neuromuscular Diseases of MD-Paedigree, meaning taking care for consistent input to the ICT developers, and coordination the supporting clinical activities.

Key personnel

Jaap Harlaar, Prof., (M), Head of the clinical movement laboratory, 27 years of expertise in movement analysis& clinical interpretation, designing user oriented software tools, invented multimedia movement analysis in the early 90's. He will be leader of WP5; coordinator of NND activities; president of ESMAC (European Society for Movement Analysis in Adults and Children).

Jules Becher, Prof., (M), Chair in paediatric rehabilitation. 30 years of expertise in Cerebral Palsy; clinical leader in the The Netherlands, conducted many RCT's; was the first in NL to apply several innovative treatments for CP (eg. Selective Dorsal Rhizotomy). HE will be the main clinical advisor in the project.

Marjolein van der Krogt, PhD, (F), staff Member. 7 years of expertise in applied clinical movement analysis and computational biomechanics of gait, introduced multiple walking speed gait analysis. She will warrant the relation between clinical protocols and the input needs of advanced disease modelling; strong links with Stanford University in modelling.

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PARTNER 7: UMC Utrecht - UNIVERSITAIR MEDISCH CENTRUM UTRECHT – University - The

Netherlands

Organisation Description

Organisation description: The Wilhelmina Children’s Hospital of the University Medical Centre Utrecht (UMC Utrecht) is one of the leading institutes in paediatric immunology in Europe. The department is renowned for its research in the field of human autoimmune diseases. For years the group has been working on immune regulatory aspects of Juvenile Idiopathic Arthritis and its main focus is immune tolerance and biomarker research.

Expertise: UMC Utrecht is an international expertise centre for the multiplex immuno assay or Luminex®. UMC Utrecht, among others as core facility for the Immune Tolerance Network of NIH, developed a high throughput custom-made toolset for soluble biomarkers using the Luminex platform (first keynote paper with > 300 citations). For this purpose UMC Utrecht used the Luminex technology to develop a “home-brew” assay for the determination of over 100 soluble factors, mostly cytokines and all directly related to inflammation, and proteins (mostly adipokines) that co-determine risk factors for the development of cardiovascular diseases in children with JIA

Facilities: UMC Utrecht is equipped with Luminex core facility

Other participation to European projects and Networks of reference : Thanks to its expertise in biomarker research, UMC Utrecht is also hosting the first international platform for biological studies in childhood arthritis, UCAN-U (www.ucan-u.org). Moreover UMC Utrecht is coordinating two EU FP 7 programs that are highly complementary to the MIAMI application, namely PHARMACHILD (Pharmacovigilance network for safety of biological agents in childhood arthritis) and EUTRAIN (Marie Curie Integrated Training Network in translational research in paediatric autoimmunity). Prof. Prakken is also scientific director of the Eureka Institute for Translational Medicine (www.eurekainstitute.org) that can assist to provide specific training in translational medicine for researchers in this project.

Key personnel

Berent Prakken, Prof, PhD, (M), Head Center of Molecular and Cellular Intervention (CMCI) UMC Utrecht. Prakken will coordinate and supervise laboratory research and provide the link with EUTRAIN and EUREKA.

Nico Wulffraat, MD, (M), Head of Department of Paediatric Immunology UMC Utrecht. Wullffraat is responsible for supervision of the clinical studies and will provide the link with Pharmachild.

Wilco de Jager, PhD, (M), Head Luminex core facility. De Jager will be responsible for development and technical quality control.

Jenny Meerdink, Eng, (F), Technician. Jenny Meerdink will be performing the assays and supervising junior technicians and PhD students in the lab.

PARTNER 8: SAG “SIEMENS AG”– “INDUSTRY” – “GERMANY”


General description: Siemens AG is a global electronics and electrical engineering company, operating in industry, energy, healthcare, and infrastructure & cities. Innovation is Siemens’ major growth and productivity driver. Its Corporate Technology (CT) division (3,000 researchers, >55,000 active patents) is a world leader among technology company research networks. Siemens CT offers an exciting interdisciplinary R&D environment to young people integrating them in highly qualified project teams of advanced researchers with academic degrees, engineers and technicians.

Expertise: The participating Biomedical Informatics Research Team within the Image Analytics and

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Informatics Technology Field at Siemens CT consists of 10 people and conducts research and development in machine learning, object and pattern recognition, simulation, and medical decision support systems.

Facilities: SAG will use standard PCs with modern graphics hardware to develop software for the project. Sag is equipped with multiple data servers to store large quantities of data (as medical images) and a fast network connection.

Other participation to European projects and Networks of reference: The Biomedical Informatics Research Program was involved in the EU project Health-e-Child and is currently the Project Coordinator of the EU-funded FP7 project Sim-e-Child. VPH models developed in both projects will be re-used and extended within the proposed project. In addition, the group leads the THESEUS-MEDICO research consortium on semantic web technologies in healthcare, funded by the German Ministry of Economy. Semantics technologies originating from this project will be leveraged for the VPH infostructure development.

Role in the project: Within the proposed project, Siemens AG will contribute to both, the infostructure development and the VPH model development, mainly focusing on the cardiology and neuro-muscular disease areas

Key personnel

Dr Olivier Ecabert, (M) Head of the Image Analytics Research Group. Before this position, Dr. Ecabert was Innovation Manager (2010-2013) in the Innovation department of the Angiography and Interventional X-ray Systems business unit of Siemens Healthcare working at the interface between research transfer and clinical evaluation. Dr. Ecabert has 12+ years experience in medical image processing in the industry covering 3D modelbased segmentation of cardiac images and interventional imaging. The results of his work were transferred to several innovative products for the diagnosis and treatment of cardiovascular disease. Dr. Ecabert was also overall Ecabert, project coordinator of a large scale European project from 2008-2010 (euHeart project, 17 institutions, €19M overall budget).

Maria Costa, PhD, (F) Research Scientist, joined Siemens in 2008. She specialized in 3D medical image segmentation using deformable models, and since then her participation in several EU projects has focused on automatic segmentation and registration, as well as on decision support and similarity search mostly in oncological scenarios. Within MD-Paedigree she is in charge of 3D image processing and segmentation for SAG.

Tobias Heimann, PhD, (M), recently joined Siemens as Research Scientist. From 2003 to 2008, he worked on automated medical image segmentation using 3D statistical shape models for his PhD at the University of Heidelberg. After that, he joined the EU Marie Curie RTN “3D Anatomical Human” as experienced researcher for biomechanical modelling of soft tissue. From 2010 to 2012, he led the segmentation team at the Div. Medical and Biological Informatics of the German Cancer Research Center. His main interests are automated image analysis, validation of segmentation algorithms and biomechanical modelling. His role in MD - Paedigree is to lead and supervise the image analysis tasks for SAG.

Alexey Tsymbal, Phd, (M), Research Scientist, joined Siemens in October 2006. In 2006-2010 he participated in an EU FP6 project Health-e-Child, where he lead a work package on decision support and developed a similarity search-based DSS CaseReasoner. His recent focus is on the development of clinical oncology applications. He has more than 50 peer-reviewed publications in the areas of his research interests which include machine learning, case-based reasoning, and applications of AI techniques to biomedical domains. Dr Tsymbal was a PC member at a number of related conferences (including IEEE CBMS 2005-2012, HealthInf 2011-2012, BIOSTEC 2010-2012, HealthGrid 2010-2011, ITAB/IS3BHE 2008-2010). Since 2005 he acts also as Associate Editor for IEEE Transactions on Information Technology in Biomedicine.

Michael Sühling, Phd, (M), Program Manager, joined Siemens 8 years ago. He leads the Biomedical Informatics Program within the Image Analytics and Informatics Global Technology Field in Siemens

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CT. Prior to this, he managed the development of clinical Oncology Applications at the Healthcare Computed Tomography Business Unit and led R&D projects in medical imaging while working at Siemens Corporate Research, Princeton, USA. In his current position, Dr. Sühling also serves as the Project Coordinator of the EU FP7 project Sim-e-Child.

PARTNER 9 BMR Genomics: “Biomolecular Research Genomics srl” –– “SME” – “ITALY”


General description: BMR is a spin-off of the University of Padova founded on 2004. It is leader in Italy in the field of DNA sequencing service. BMR Genomics is the first Italian service to offer this NGS and bioinformatic service to the Italian research world since 2008. It s is involved in R&D projects both internal and in collaboration with university groups, mainly with Prof. Giorgio Valle's research group (University of Padova and cofounder). BMR has a well equipped and organized molecular laboratory including liquid handler and home made LIMS. It is ISO 9001 certified for quality management of the processes.

Expertise: BMR Genomics is specialised in DNA sequencing, bioinformatics and molecular biology. 15 people work in BMR Genomcis, most of which have a bachelor degree and 3 a PhD.

Facilities: Only the main facility

Other participation to European projects and Networks of reference : BMR Genomics is partner in the european projget PF7 named Aquatrace, currently in negotiation phase.

Role in the project: BMR will contribute to the project with its wide experience in nextgen DNA sequencing and bioinformatics analysis. www.bmr-genomics.it

Key personnel

Sara Todesco, PhD, (F), born in Vittorio Veneto (TV - Italy) 16-06-1980. Degree in Biotechnology - University of Padua Votation: 110/110 cum laude. PhD in Molecular fisiology and structural biology. Currently working at BMR genomics as responsible for NGS sequencencing service.

Barbara Arredi, Dr. (F), born in Rome (Italy) the 19th of February 1974. Graduated in Biology (II Level) - University of Rome, Votation: 110/110 cum laude. PhD Degree in Medical Forensic Science (University "Sacro Cuore" of Rome). Currently working at BMR Genomics srl for Next Generation Sequencing service

Alessandro Albiero, Dr. (M), born in Arzignano (Vicenza - Italy) the 23th April 1977. Degree in molecular biology (II Level) - University of Padua Votation: 110/110 cum laude. Currently PhD Student at CRIBI (University of Padua) and bioinformatic area manager for NGS sequencer at BMR Genomics.

PARTNER 10: Fraunhofer - Fraunhofer-Gesellschaft Zur Foerderung der Angewandten

Forschung E.V – Public Research Centre – Germany


General description: Fraunhofer is Europe’s largest application-oriented research organization with 60 Fraunhofer Institutes, at different locations in Germany. Fraunhofer is the world's leading institute for applied research in the field of visual computing. Visual computing stands for image- and model-based information technology. It includes computer graphics, computer vision, as well as virtual and augmented reality. Fraunhofer develops prototypes and complete solutions based on

http://www.bmr-genomics.it/

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customer demands. The range of applications of the concepts, models, and practical solutions spans from virtual product development to medicine, commerce, and multimedia learning and training. Numerous spin-offs ensure that prototypes are quickly brought to market.

Expertise: The department of Cognitive Computing & Medical Imaging at Fraunhofer IGD has strong expertise in the field of 3D medical imaging, i.e. image enhancement, segmentation, and registration. The last years, research has been focused on the modelling of organs and anatomical structures employing statistical knowledge gathered from a variety of image training data obtained from large patient cohorts. Recently, this purely image based modelling has been extended to incorporate patient specific information from other scales, e.g. biomarkers. Furthermore, the integration of material parameters into those models employing finite-element modelling is a novel field we are working in. Its expertise it is also focused in the areas of image-based intervention and surgery, ultrasound applications for diagnosis and treatment, medical simulation and planning as well as telemedicine. Its application oriented research results routinely in demonstrators that can be evaluated in the clinics.

Facilities: Fraunhofer-IGD will use its IT infrastructure for the software development needed for MD-Paedigree. All key personnel’s workplaces are equipped with capable PC systems providing also the graphics power needed for the project. The existing in-house software framework will be the basis for the development of novel algorithms and the adaptation of available software tools.Other participation to European projects and Networks of reference: Fraunhofer IGD was (through its Competence Center “Cognitive Computing & Medical Imaging”) the coordinator of several European projects on eHealth; TeleInViVo (IST Prize 2001), T@LEMED, Mednet and has participated as member of numerous consortia, e.g. @HOME project ACGT - Advancing Clinico-Genomic Trials on Cancer NeoMark ).

Role In the project: The cohort for the study will be children and since whole body imaging should be taken, radiation free modalities need to be employed. Thus, MR and ultrasound imaging will be used. Fraunhofer IGD will develop automatic tools for the extraction of relevant regions identified as relevant for the study by clinical experts. In addition to that, Fraunhofer IGD will build patient specific anatomical models and will quantify the amount of fat. Modelling based on image datasets also requires that this data is registered with each other, requiring the consideration of deformation that definitely will occur. The anatomical models will be provided to the other partners in order to combine them with additionally acquired information, e.g. determined risk for CVD, other diseases (e.g. diabetes) and classification of the JIA patients. Furthermore Fraunhofer IGD will work on the image processing algorithms for automatically detecting regions of interest and extracting biomarkers from the MRI and ultrasound imaging data of the joints.

Key personnel

Stefan Wesarg, PhD, (M), studied Physics at the Technische Universitaet Berlin, Germany, the Ecole Nationale Supérieure de Physique de Marseille, France, and the Universitaet Heidelberg, Germany. His research activities comprised image segmentation and analysis, augmented reality systems for medical use, and medical simulation systems. He is regularly working as reviewer for peer-reviewed journals as well as conferences and workshops (e.g. IEEE Visualization, EuroVis, MICCAI Workshops.

Cristina Oyarzun Laura, Eng, (F), studied Telecommunications Engineering at the University of Navarra, Spain. She wrote her diploma thesis at Copenhagen University College of Engineering, Denmark. After receiving her diploma degree in 2007 she started to work as researcher in the Department Cognitive Computing & Medical Imaging at Fraunhofer IGD in Darmstadt, Germany.

Matthias Keil, Eng, (M), studied Computational Visualistics at the Otto-von-Guericke-University in Magdeburg, Germany. After receiving his diploma degree in 2005 he spent 2 years in industry before joining Fraunhofer IGD as a research associate in 2008. His research area is medical imaging, in particular ultrasound image processing, multimodal image registration and intraoperative navigation in surgical interventions.

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PARTNER 11: INRIA - Institut National de Recherche en Informatique et en Automatique –

Public Research Centre – France


General description: Established in 1967, INRIA is a French public research body fully dedicated to computational sciences. Combining computer sciences with mathematics, INRIA’s 3,400 researchers strive to invent the digital technologies of the future. Educated at leading international universities, they creatively integrate basic research with applied research and dedicate themselves to solving real problems, collaborating with the main players in public and private research in France and abroad and transferring the fruits of their work to innovative companies. The researchers at INRIA published over 4,800 articles in 2010. They are behind over 270 active patents and 105 start-ups. In 2010, INRIA’s budget came to 252.5 million euros, 26% of which represented its own resources.

Expertise: The INRIA Asclepios team has three main objectives: (i) analysis of biomedical images with advanced geometrical, statistical, physical and functional models, (ii) simulation of physiological systems with computational models built from biomedical images and other signals, and (iii) application of previous tools to medicine and biology to assist prevention, diagnosis and therapy. This research team, led by Pr. Nicholas Ayache, has over twenty years of experience in medical image analysis and medical simulation.

Facilities: Cluster of computers, standard computing equipment.

Other participation to European projects and Networks of reference :The Asclepios team was involved in a number of passed European projects including Health-e-Child (Passport (3D Anatomical Human and is still involved in the VPH NoE), euHeart Care4Me and the ERC MedYMA (2012-2017). The team is also involved in a French ANR projects ANR blanc Karametria (2010-2013).

Role in the project: The Asclepios team at INRIA developed in the passed a number of electro-mechanical models of the beating heart that can be fitted to the patient geometry. More recently, an important work was performed on the personalization of the physical and physiological parameters in order to obtain patient specific simulations of the beating heart. The role of the Asclepios team will be to adapt these models to the diseases (cardiomyopathies and obesity) and to couple the model with computational fluid dynamic models in order to obtain an integrated model. In order to do so, we intend to personalize the existing model on the large database of subjects that will be acquired in the MD-Paedigree project. Statistical learning techniques will then be used to extract the meaningful physical and physiological parameters as the disease level.

Key personnel

Xavier Pennec, PhD, (M), Senior Research Scientist at INRIA, 19 years of research. Expertise on Geometric statistics, Non-linear Image Registration, Computational Anatomy, Medical Image Analysis. Major discoveries in statistical computing on manifolds, Diffusion tensor imaging, Shape statistics. He is PI at INRIA.

Maxime Sermesant, PhD, (M), Research Scientist at INRIA, 10 years of research. Expertise on Herat modelling, Medical Image Analysis.Major discoveries in cardiac function modeling. Scientific expertise on cardiac modeling.

PARTNER 12: Motek - Motek Medical – SME – The Netherlands


General description: Technology provider

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Expertise: Motek Medical provides innovative products for rehabilitation, orthopaedics, neurology, performance enhancements and research. Integrated virtual reality environments that combine motion platforms, dual-belt instrumented treadmills, motion-capture systems and surround sound enable movement function evaluation and stability enhancement. Game elements and rich immersive interactions ensure an optimal user presence for patients.

Facilities: The technology uses multi-sensory real-time feedback and offline analysis tools. Motek developed and continually improves a software platform called D-Flow (Geijtenbeek et al., 2011). Key concept of the D-Flow software system is that the subject is regarded as an integral part of a real-time feedback loop, in which multi-sensory input devices measure the behavior of the subject, while output devices return motor-sensory, visual and auditory feedback to the subject. The D-Flow software system allows users to define feedback strategies through a flexible and extensible virtual reality application development framework, based on visual programming. Within the D-Flow software the biomechanical Human Body Model (HBM) is implemented for the analyses of human gait combined with a real-time visualization on muscular activity.

Other participation to European projects and Networks of reference: MOTEK is involved in the NeuroSIPE program www.neurosipe.nlwww.neurosipe.nl in which system identification protocols are to be implemented in real-time feedback applications for low back pain, neck instability and balance control. Furthermore Motek supports and collaborates in the joint doctorate program Move-Age focusing on research related to mobility and aging http://www.move-age.eu/. New products based on scientifically proven paradigms will be developed. Both programs were interested in collaborating with Motek because of the D-Flow software platform which enables them to easily construct and test applications with different types of hardware and real-time feedback in a Virtual Reality environment.

Role in the project: MOTEK is workpackage leader (WP 11)in the MD-Peadigree project. In this role Motek will implement the clinical data obtained from the clinical partners to enhance the Human Body Model and the interpretation of the output data. Data obtained from patients will be used to adapt the HBM to enable better predictions of the model and to construct relevant applications which will indigate pathology specific aspects of the patients.

Key personnel

Frans Steenbrink, PT, PhD (M), is working at MOTEK as a part of the New Product Development team. Educated and mastered in Human Movement Sciences in the context of Rehabilitation with a background in Pysiotherapy Frans has been involved in the development of new applications and products based on the real-time feedback philosophy of MOTEK.

Oshri Even-Zohar, CTO (M), is the founder of MOTEK and an inventor and animator by trade; Since 2005 Oshri is also active as an advisor for complex visualization projects and is engaged in the development of patient treatment programs and virtual environments for the SHEBA medical center in Tel Aviv Israel. Oshri will consult and advice the developments related to the MD-Peadigree both on software HBM code level as on application level.

Thomas Geijtenbeek, MSc – Lead Software Development, (M), in 2004, became a full-time employee. As of 2005, he is in charge of all software design and development at MOTEK. He is currently focusing on the continuous improvement of the D-Flow software and the supervision of all future coding activities. Momentarily Thomas is also executing a scientific research program on the use of biomechanics in animations. In his role as lead software programmer Thomas will be in charge of all programmers who will be working on MD-Peadigree related software developments on the HBM adjustments and he will communicate with Frans about the related implementation.

PARTNER 13: SCR - SIEMENS CORPORATION – Private Research Centre – USA

http://www.neurosipe.nl/

http://www.move-age.eu/

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General description: Siemens Corporation, Corporate Research and Technology, (SCR) is a world-class research and development centre within Siemens Corporate Technology located in Princeton, New Jersey, USA. It is comprised of more than 300 of the world’s most talented scientists, engineers and technical experts from over two dozen countries. Cross-functional research teams bring together a broad range of technical and business expertise from different disciplines to support Siemens' broad range of businesses, especially in the fields of medical imaging and bioinformatics.

Expertise: The Image Analytics & Informatics team develops leading-edge technologies for integrating and representing a wide range of information. Our unique, vertical integration of modern statistics, modeling, optimization and advanced database techniques brings a competitive advantage to the customers. Our research supplies technologies that enable the personal and predictive medical practice of the future. Its focus is on data representation, integration, exchange, analysis, and management in the biomedical domain. Collaborating with leading universities, we focus on transforming theoretical results into advanced technology and innovative intellectual property for Siemens.

Facilities: SCR will use standard PCs with modern graphics hardware to develop software for the project. SCR is equipped with multiple data servers to store a large quantities of data (as medical images) and a fast network connection.

Other participation to European projects and Networks of reference : The Image Analytics & Informatics team was involved in the EU-funded FP7 project Sim-e-Child mainly focusing on cardio-vascular haemodynamic modelling and web-based data repository development. This prior work will be re-used and extended within the proposed project. In addition, SCR is involved in numerous US government-funded research projects such as the cancer Biomedical Informatics Grid (caBIG) Program, an open access information network with the mission of enabling secure data exchange throughout the cancer community, supported by the National Institutes of Health (NIH).

Role in the project: Within the proposed project, Siemens Corporation, Corporate Research and Technology, will mainly contribute to the VPH modelling and simulation development focusing on the cardiology disease area.

Key personnel

Dorin Comaniciu, PhD, (M), is Global Technology Head for Image Analytics and Informatics at Siemens Corporate Technology, His scientific interests include medical imaging, cardiac modeling, whole body, image-guided surgery, and biomedical informatics. He is a Fellow of the IEEE and Top Innovator of Siemens AG. He holds 100 US and international patents and has co-authored more than 200 publications in the area of information processing, including best papers in CVPR and MICCAI. Dr. Comaniciu received the 2011 Thomas Alva Edison Award for a patent on heart modeling, the 2010 IEEE Longuet-Higgins Prize for 'fundamental contributions to computer vision', and served as the scientific director of Health-e-Child, a project granted the 2008 Europe's Information Society Grand Prize. The aortic valve implantation technology his team contributed to Siemens received the 2010 Innovation Award of the European Association for Cardio-Thoracic Surgery. He served as an Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Medical Imaging. He graduated from University of Pennsylvania - The Wharton School (AMP'11), Rutgers University (PhD'99), and the Polytechnic University of Bucharest (PhD'95).

Tommaso Mansi, PhD, (M), Research Scientist, received his Ph.D. degree in biomedical engineering from Ecole des Mines de Paris, Sophia-Antipolis, France in 2010 as a result of his research activity at INRIA Sophia Antipolis, Asclepios Research Team. Just after graduation, he joined the Image Analytics and Informatics global technology field at Siemens Corporation, Corporate Research and Technology. During his Ph.D. at INRIA, Tommaso was strongly

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involved in Health-e-Child FP6 IP project. His work within the project resulted in several scientific publications about therapy prediction for pulmonary valve replacement and statistical model of right ventricle growth in Tetralogy of Fallot. At Siemens Corporation, Tommaso is currently investigating multi-scale computational models of cardiovascular physiology for advanced diagnostics and therapy planning. He applied this approach to simulate mitral valve intervention, for which he earned the Young Scientist Award at MICCAI 2011 conference. Tommaso has co-authored 34 scientific publications in the area of medical image analysis and computational physiology.

PARTNER 14: TU Delft –TECHNISCHE UNIVERSITEIT DELFT – UNIVERSITY – The

Netherlands


General description: Delft University of Technology is the largest engineering university in the Netherlands, founded in 1842. There are 15 faculties, one of them is the Faculty of Mechanical, Maritime and Materials Science. In this faculty the dept. of Biomechanical Engineering (BMechE) is hosted. The BMechE dept. is involved in the MSc Biomedical Engineering, together with the faculties of Applied Sciences and Electrical Engineering, and in the MSc Mechanical Engineering with a track in Biomechanical Design, with the specialisations Bio-Robotics, Biocompatible design and Intelligent Machine Systems. There is a close collaboration with the Leiden University Medical Center (LUMC) and the Erasmus Medical Center (EMC) Rotterdam.

Expertise: The Department of Biomechanical Engineering focuses on biomechanical and biocontrol models of the musculoskeletal system, on the development of novel measurement instruments and data processing methods, and on experiments on human motion control. A three-dimensional large-scale neuromuscular model of the upper extremity (shoulder and elbow) has been developed. Model parameters have been recorded in extensive cadaver measurements and MRI measurements. In the model, advanced muscle dynamic models are present, as well as proprioceptive feedback models of muscle spindles and Golgi tendon organs. Clinical applications are, amongst others, the effect of tendon transfers, joint implants, functional electro-stimulation, scapular and clavicular fractures. The research group was co-founder of the International Shoulder Group (ISG), a technical group of the International Society of Biomechanics. In the ISG, the Delft research group initiated standardization of motion recording methods and cadaver recording methods, in order to ensure the possible exchange of data. In the dept. of BMechE there is also experience with MRI measurements, FE modelling, biomechanical models of the pelvic floor musculature and the eye musculature.

Facilities: Motion recording equipment (Qualisys), EMG recording equipment (Delsys, TMSi), robot manipulators for neuromuscular control recordings

Other participation to European projects and Networks of reference : STIFF, EVRYON, MindWalker, MXL. http://www.bmeche.tudelft.nl/

Role in the Project: TU Delft team will contribute to the musculoskeletal modelling module as well as the development of the functional tests and the patient specific biomechanical simulations of the shoulder in the prospective validation tasks of the project.

Key personnel

Frans van der Helm, Prof, (M), is Professor in Biomechatronics and Bio-robotics (0.8 fte at Delft, and 0.2 fte at the University of Twente). He has a MSc in Human Movement Science (1985), and a PhD in Mechanical Engineering (1991). He participates in the board of the Technical Group of Computer Simulation (ISB) and the International Shoulder Group. He is a programme leader at the Delft Centre of Biomedical Engineering. He is Principal Investigator in the TREND research consortium, consisting of 5 university hospitals and TUD, with 35

http://www.bmeche.tudelft.nl/

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participants and 11.3 mEuro. He is head of the BMechE department. He is heading two national programs: NeuroSIPE (System Identification and Parameter Estimation of Neurophysiological Systems, 5.25 mEuro) and H-Haptics (Human center Haptic Interfaces, 4.75 mEuro). He recently received an ERC advanced grant for research into 4D-EEG: Improving spatial and temporal assessment of EEG source localization using mechanical perturbations.

PARTNER15: URLS - Università degli Studi di Roma “La Sapienza –University – Italy


General description: URLS was founded in 1303 by Pope Boniface VIII, it is the first University in Rome and the largest University in Europe: a city within a city, with over 700 years of history, 145,000 students, over 4,500 professors and almost 5,000 people are administrative and technical staff. URLS has a wide academic offer which includes over 250 degree programmes and 200 one or two year professional courses. Concerning with students’ origin, over 30,000 of them come from all parts of Italy; over 7,000 people come from abroad. Incoming and outgoing Erasmus students are about 1,000 people per year.

Expertise: the Dept. of Mechanical and Aerospace Engineering plans and carries out relevant scientific investigations in almost all disciplines related to Mechanical and Aerospace research areas achieving high-standard results both on a national and on an international level, thanks of the work of over 70 core faculty members. The Biomechanical Research Unit has considerable strength in experimental Biomechanics and a particularly strong expertise in the development of: innovative experimental methods to assess the pathology severities and novel mechanical systems for robot mediated therapies.

Facilities: the Lab is fully equipped with the state of art of hardware and software to permit an in-house design and making of innovative systems.

Other participation to European projects and Networks of reference:

Prof. Paolo Cappa research funding related to MD-Paedigree project (2006-present)

1) Italian Ministry of Public Education, University and Research (MIUR) – PRIN 2006 “Novel experimental methods for the optimization of rehabilitation treatments” (200k€, PI, Co-PIs affiliated to Polytechnic of Milan, Marche Polytechnic University Ancona, University of Rome III Rome, and Campus Biomedico Rome, two years).

2) Italian Ministry of Health: “Effectiveness of the robot mediated therapy for upper limb” (2007, PI, 300k€, one year) - “Development of novel robot system for dynamic posturography” (2008, PI, 300k€, one year) - “Development of a novel knee exoskeleton for children with Cerebral Palsy” (2009, PI, 300k€, one year).

3) Italian Institute of Technology (IIT) – Project Seed “ITINERE interactive technology: an instrumented novel exoskeleton for rehabilitation” (2009, PI, 670k€, three years).

Several researchers enrolled at the Department of Mechanical and Aerospace Engineering were financed by the UE in the FP7 and the most recent projects are as follows: LAPCAT-II, ISP-1, TFAST, MID-FREQUENCY.

Role in the project: URLS will identify the variables related to gait analysis to be stored in the repository taking into account the specificities of gait patterns in rheumatoid arthritis and in children with hemiplegia. Moreover, URLS will made available his expertise in Experimental Biomechanics to select additional measuring systems for the evaluation of further variables identified as crucial in patient-specific modelization.

Key personnel

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Paolo CAPPA, Prof, Eng, (M),at DIMA – URLS with a secondary appointment as a Research Professor at the Dept. of Mechanical and Aerospace Engineering of Polytechnic Institute of New York University (NYCPoly). He established the BRU at DIMA and contribute at the development of a multidisciplinary team at Movement Analysis and Robotics Laboratory that consists of 20 people. Over the last 10 years he has developed the following main areas: (1) the proposal of innovative measuring system to gather kinematic and kinetic data; (2) data validation of systems used in Experimental Biomechanics; and, finally, (3) the design and making of novel mechanical systems for robot mediated therapy. In these fields he is author of more than 90 peer-reviewed journal publications. He is PI in national research grants provided by Ministry of Health, Italian Ministry of Education, University and Research, Italian Institute of technology, and private companies.

Fabrizio Patané, MD, PhD (M), Specialist in Experimental Biomechanics, Experimental Mechanics, and Rehabilitation Robotics. Currently is associate researcher and authored 12 peer reviewed publications and one European patent for a dynamic posturography robotic system.

Stefano Rossi, MD, PhD (M), Specialist in Experimental Biomechanics, Experimental Mechanics. Rehabilitation Robotics. Currently is associate researcher and authored 6 peer reviewed publications.

PARTNER 16: USFD - University of Sheffield– Public Research Institution - United

Kingdom


General description: The University of Sheffield (USFD) is the Times Higher Education UK University of the Year in 2012. Official research assessments confirm its reputation as a centre for world-class research in many disciplines. It has more than 24,000 students from 118 countries, and almost 6,000 staff. The University has recently made ~3M pounds investment in the Virtual Physiological Human arena, with the establishment of a new Institute spanning the Faculties of Engineering and Medicine and the Sheffield Teaching Hospitals Trust.

Expertise: The participating academics have contributed strongly contributing at each stage in the evolution of the Virtual Physiological Human (VPH), including co-authorship of the November 2005 White Paper, edited jointly by DG INFSO and DG JRC, and of the roadmaps that have informed its progress. Specific relevant expertise is evidenced in ‘other participation’ below.

Other participation to European projects and Networks of reference: USFD lead scientists coordinated some key VPH projects including @neurIST, VPHOP, and the STEP support action that wrote the VPH roadmap. They are also all core partners in the VPH-NOE, and lead two work-packages in EU-HEART. Most importantly for MD-Paedigree, the participating academics include the scientific coordinators of VPH-Share, one of the two VPH infrastructure integrated projects.

Role In the Project: The Faculty of Medicine engages the Scientific Coordinator and the Architecture lead from VPH-Share. This will ensure that MD-Paedigree i) makes optimal re-use of relevant VPH-Share scientific developments, ii) contributes data and tools back to the community wherever this is strategically and ethically possible, iii) leverages the communication network of VPH-Share to engage with the wider VPH community.

Key personnel

Rod Hose, Prof, (M), has a BSc in Mathematics and PhD in Applied Mechanics from the University of Manchester. He worked in the aeronautical and engineering consultancy industries for over 12 years before joining Medical Physics in Sheffield in 1994. He is the Scientific Coordinator of the infrastructure project VPH-Share, and served as a work package leader in

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@neurIST and in euHeart.

Steven Wood, PhD, (M), has a PhD in Medical Physics and is the Head of Scientific Computing in the Sheffield Teaching Hospitals Foundation Trust. He is the Architecture lead in VPH-Share.

Marco Viceconti, Prof, (M), is full Professor of Biomechanics at the Department of Mechanical Engineering at the University of Sheffield. Before this he was the Technical Director at the Rizzoli Orthopaedic Institute in Bologna, Italy. His main research interests are related to the development and validation of medical technology, especially that involving simulation, and primarily in relation to musculoskeletal diseases. Marco Viceconti is one of the key figures in the emerging Virtual Physiological Human (VPH) community, currently chairing the Board of Directors of the VPH Institute.

Alejandro Frangi, Prof, (M), is a Professor of Biomedical Image Computing at the University of Sheffield (USFD). He is also , where he is the General Director of of the Center for Computational Imaging and Simulation Technologies in Biomedicine (http://www.cistib.org/) and a member of INSIGNEO Institute for in silico Medicine Biomedical Imaging & Modelling (www.insigneo.org), both at USFD the University of Sheffield, a cross-faculty interdisciplinary research institute in the Virtual Physiological Human domain. His main research interests are in medical image computing, medical imaging and image-based computational physiology. DrProf. Frangi has been principal investigator or scientific coordinator of over 20 national and European projects. DrProf. Frangi has extendedly extensively contributed to the VPH community through such by participating in initiatives such as the VPH integrated projects @neurIST (as Scientific Co-coordinator) and euHeart or the VPH Network of Excellence.

PARTNER 17: maat G - MAAT France – Industry – France


General description: MAAT France is a consulting firm delivering ICT solutions and professional services axed on Grid and Cloud computing with a special emphasis on e-Health and Health Information Systems. maat G justifies of a rich expertise in the establishment and execution of multi-partner collaborations, which it has capitalized upon its involvement in several international initiatives. maat G is a member of the Spanish maat G Technology group – for which it is managing the biomedical applications line.

Expertise: MAATG Technology, on a larger scale, provides cross-sectorial solutions including but not restricted to Information System, Customer Relationship Management and Data Integration for Public Administration, Financial Institutions and Small and Medium Enterprises. maatG is implanted in Argonay in France, in Valencia in Spain and in Bucaramanga, in Colombia.

Other participation to European projects and Networks of reference : maat G is involved in Sim-e-Child, a European FP7 funded project, (following on the EU FP6 Health-e-Child), in DECIDE, a European FP7 funded. maatG is also the principal technical partner ofoutGRID, a European FP7 funded international cooperation and in Ginseng project, a French National funded project as technical partner.

Role in the project: maat G responsibilities in the present proposal are: Technical Supervision, leading Fact Finding and interoperability Analysis workpackage (WP2), and contribution to the design and feasibility study, in particular in architecting the global e-infrastructure.

Key personnel

David Manset, Phd,(M), CEO of maat G France and Director of Biomedical Applications at MAATG Technology group. Mphil in Computer Sciences, Artificial Intelligence. Working in the

http://www.insigneo.org/

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field for 9 years, his main research interests are: Grid technologies, Model-Driven Software Engineering. Technician:

Jerome Revillard, Phd, (M), PhD in Computer Sciences, Grid technologies, formal software architecture modelling methods. Technician:

Sebastien Gaspard, Phd, (M), Mphil in Computer Sciences, Artificial Intelligence, engineer in General Computer Science, expert in relational DB and technical project management.

PARTNER 18: HES-SO - Haute Ecole Spécialisé de Suisse Occidentale– University –

Switzerland


General description: The HES-SO (University of Applied Sciences Western Switzerland) is currently the largest University of Applied sciences in Switzerland with over 16,000 students. The school has several campuses from Geneva to Neuchatel and Sierre. In this project the two campuses in Geneva and in Sierre are involved with the domains information sciences and business informatics. The University has in general a strong experience in the domains of information management, service sciences, life sciences and software engineering.

Expertise: The BITEM group in Geneva and the MedGIFT group in Sierre are involved in this project. Both have an important experience in medical semantic interoperability and medical information management. The BITEM group has worked for the past over 6 years on text engineering and textual information retrieval, including an important use of medical terminologies and large-scale information analysis. The MedGIFT group in Sierre, has a main focus on image management and image information retrieval. As organizer of the ImageCLEF benchmark another focus has been on technology assessment and information retrieval evaluation, particularly regarding medical information.

Facilities: The eHealth unit in Sierre has computing facilities that can be used for the project including around 6 TB of distributed storage a Hadoop/MapReduce cluster with 72 processors and a strong server with 96 GB of RAM and 12 processors. The BiTeM group is affiliated to the Swiss Institute of Bioinformatics and has access to the Vital-IT high-performance computing platform (~5% of 1200 CPU, more than 2 Tflops peak, 4 TB RAM, 800 TB Storage, 2 PB).

Other participation in European projects and Networks of reference: Henning Müller had been involved in several FP6 projects such as KnowARC, AneurIST, Multimatch and TrebleCLEF. Currently Henning is involved in the Chorus+ coordination action and the PROMISE network of excellence and is coordinating the integrate project Khresmoi. Patrick Ruch has also been involved in several EU funded projects, notably GALEN, GALEN-IN-USE, WRAPIN, and SemanticMining, and currently also the Khresmoi, DebugIT and epSOS projects.

Role in the project: tHES-SO has as main roles the management of large parts of the MD-Paedigree infostructure and the lead in two work packages (WP 13 and 15) related to the semantic interoperability of the data and the assessment and management of system requirements and compliance with other major data infrastructures in the field. The information retrieval aspects including text analysis and visual information retrieval are other roles of the HES-SO. This includes the retrieval of similar cases combining visual and textual cues. The potential use of the resources for clinical trials will be supported by the knowledge of the HES-SO in this domain and its connections with the EHR4CR’ Innovative Medicine Initiative project.

Key personnel

Henning Müller, Prof, (M), studied medical informatics at the University of Heidelberg, Germany, and then worked at Daimler-Benz research in Portland, OR, USA. From 1998-2002 he worked on his PhD degree at the University of Geneva, Switzerland with a research stay at

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Monash University, Melbourne, Australia. Since 2002 Henning has been working for the medical informatics service at the University hospitals of Geneva. Since 2007 he has been at the HES-SO Valais and since 2011 he is responsible for the eHealth unit.

Patrick Ruch, Phd, (M), received his PhD in computer science from the University of Geneva in 2002. Since 2008, he is full-time faculty at the department of information sciences (University of Applied Sciences Geneva, HES-SO Geneva), where he heads the Bibliomics and Text Mining (BiTeM) group. In parallel, he is associate researcher at the University Hospitals of Geneva. The group is associated to the Swiss Institute of Bioinformatics and maintains several text-based applications for biocurations of molecular biology resources such as GOCat (3.2 million queries in 2011) and EAGLi.

Arnaud Gaudinat, Dr,(M), is a Senior Researcher at the HES-SO Geneva where he works on research projects to advance information retrieval on the Web. Formerly he worked at the Health On the Net Foundation (10 years) where he was in charge of the research and development of the non-governmental (and the EU WRAPIN project).

PARTNER 19: UTBV - Universitatea Transilvania din Brasov – University – Romania


General description: Universitatea Transilvania din Braşov is a Romanian public academic institution with about 920 full-time scientists and around 21,000 students (including 1,200 students in doctoral programs). The university now has 16 faculties, eight in engineering fields and eight in various academic profiles. The Research Departments are defined in the strategy as autonomous structures of the university, responsible for developing quality and competitive research. The research departments have their own staff, Ph.D. students, research master programs and high-level infrastructure.

Expertise: The Department of Automation is part of the Faculty of Electrical Engineering and Computer Sciences of UTBV. The department coordinates license programs (Automation&Applied Informatics, Information Technologies) and master programs (Advanced Systems in Automation, Information Technologies). The research activities carried out by the staff, M.Sc. and Ph.D. students takes place in the Product Development Department D9- Process Control Systems- with its two units Software Systems and Process Intelligent Control. Distributed and Parallel Computing is one of the relevant research directions.

Facilities: GPU based computational cluster

Other participation to European projects and Networks of reference : UTBV has been involved in major European projects and programs since Romania was accepted after 1990 to participate in competitions (Copernicus, Phare, FP6, etc.).

Role in the project: UTBV will contribute mainly to GPU related algorithm and infostructure developments.

Key personnel

Constantin Suciu, Prof, (M), received the Dipl.-Eng. Degree in Electrical Engineering in 1994, the M.Sc. degree in Electrical and Computer Engineering in 1995- all from UTBV- and the Ph. D. degree in Electronics and Computer Engineering in 2000 from the Nottingham Trent University, UK. He is currently an Associate Professor/Reader at UTBV. He has worked on a number of European/National or Industry funded R&D projects. He is author or co-author of more than 50 scientific publications in international conferences and journals. His current research interest is focused on the areas of Distributed Control&Processing, Embedded Systems including ASIC/FPGA/GPU based architectures and Artificial Intelligence.

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Adrian Postelnicu, Eng, (M), holds degrees in aircraft engineering and mathematics from University Politechnica Bucuresti and University Bucuresti respectively, and was awarded with PhD title in Fluid Mechanics. Currently, he is a Professor at UTBV. He has been involved in various reasearch projects handling aspects such as Viscous Fluid Flows, Hydrodynamic Stability, Turbulence modelling, Boundary layer control, Biomechanics or cerebral haemodynamics. He is main author or contributor to over 100 scientific articles/papers.

Lucian Mihai Itu, Eng, (M), was born in Bucharest, Romania, in 1985. He received the Dipl.-Eng. Degree in Automatics and Applied Informatics from the “Transilvania” University of Brasov, Brasov, Romania, in 2009. He is currently a PhD student in the area of System’s Engineering at the “Transilvania” University of Brasov. He is author or co-author of more than 15 scientific publications in international conferences and journals. His current research interests include: blood flow simulations, coronary circulation and parallel computing.

Florin Moldoveanu, Eng, (M), received a Dipl.-Eng. Degree in Electrical Engineering from Politechnica Institute of Brasov, Romania in 1975, and a Ph.D. degree in Electrical Engineering from UTBV, Romania in 1998, with a thesis on Control Engineering. He joined in 1990 the Department of Automation at UTBV where he is currently Professor.

PARTNER 20: ATHENA - ATHENA Research and Innovation Center in Information

Communication & Knowledge Technologies – Research Center – Greece


General description: The“ATHENA Research and Innovation Center in Information, Communication and Knowledge Technologies” (ATHENA) was founded in 2003 comprising of three previously existing and two new institutes. It operates under the auspices of the Ministry of Education and Religious Affairs, Culture and Sports. It is based in Athens, with its Institutes located in Athens, Patras and Xanthi. Its aims include the following: (a) development of scientific and technological research in information, communication, and knowledge technologies; (b) combination of research and development of core technologies in areas where there is significant market potential, competitive advantage and local needs (language, culture, education, embedded systems, networks, large databases and information systems, space technologies); (c) creation of a fertile technological innovation ecosystem, focusing on modern information technology fields that have open research challenges and offer innovation potential.

Expertise: ATHENA has been cooperating in hundreds of R&D projects with partners from all over the world. It boasts a team of high-profile researchers and collaborators, including IEEE and ACM Fellows, some with h-index > 40, occupying high-profile offices in international organisations and with several best paper awards. The team has a rich and long experience in several topics of computer Science. The current focus of research at ATHENA includes: digital libraries, user interfaces, personalization, databases and knowledge bases, workflows for data integration and system interoperability, ontology engineering and data warehouses, software engineering, experiment management systems, data mining, large scale distributed information systems, web information systems, web data management, semantic web technologies and geographic information systems with a focus on data integration and user contributed information, industrial processes, security, privacy and critical systems, language technologies, cognition in the area of robotics, assistive technologies, space exploration engineering and environmental data management.

Other participation to European projects and Networks of reference: projects funded during the past years include CARARE (ICT FP7), TALOS (FP7), DARIAH (INFRA FP7), LeMPEM (Marie Curie), Biosec (Marie Curie), GRDI (INFRA FP7), R2D2, Earth-server (INFRA FP7),ESPAS(INFRA FP7), Interact(INFRA FP7), DILIGENT Integrated Project (IST FP6), IAS (eContent), DELOS Network of Excellence (IST FP6), DRIVER Targeted Project (IST FP6),

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BRICKS Integrated Project (IST FP6), KATOPTRON, DIAVION (under Greek initiatives), Health-e-Child Integrated Project (IST FP6), TELplus Targeted Project (ECP FP6), DRIVER II CP/CSA (INFRA FP7), BELIEF I SSA (IST FP6), BELIEF II Coordination Action (INFRA FP7), D4Science CP/CSA (INFRA FP7), D4Science II CP/CSA (IP FP7), PAPYRUS Collaborative Project (ICT FP7), DL.org (ICT FP7), Open AIRE (IP ICT FP7), OpenAIRE plus (INFRA FP7), CHESS (ICT FP7), eNVENTORY Coordination Action (INFRA FP7), Mermaid (OCEAN FP7), imarine (INFRA FP7), T4ME (ICT FP7), QTLaunchPad (ICT FP7), CLARIN (INFRA FP7), DICTA-SIGN (ICT FP7), ACCURAT (ICT FP7), PRESEMT (ICT FP7), POETICON/POETICON++ (ICT FP7).

Role in the project: ATHENA leads WP16 on Biomedical knowledge discovery and simulation for model-guided personalised medicine, bringing all the experience gained during the Health-e-Child project, where the same research team (at the time affiliated with University of Athens - UoA) lead the equivalent workpackage on Biomedical Knowledge Discovery & Data Mining (leading tasks T16.2 PAROS Personalization Platform and T16.3 AITION Knowledge Discovery & Simulation Framework). In MD-Paedigree, ATHENA also leads WP17 on Testing and validation, whilst also participating in: WP9 Modelling cardiovascular risk in the obese child and adolescent; WP 13 Requirements and compliance for the MD-Paedigree Infostructure (leading task T13.4 Data policy definition and implementation); WP 14 Grid-Cloud Services provision and GPU services integration (leading task T14.3 ATHENA Distributed Processing (ADP) Engine Integration); WP15 Semantic data representation and information access (leading tasks T15.1 Data curation & validation tool, T15.4 Ontology-based querying); WP18 - Dissemination & Exploitation; and WP19 - HTA and Medical clearance.

Key personnel

Yannis Ioannidis, Prof, (M), is Professor at the Department of Informatics and Telecommunications of the University of Athens. In 2011, he also became the President and General Director of the ATHENA Research and Innovation Center; in addition, since April 2011, he serves as the Acting Director of the Institute of Language and Speech Processing of ATHENA. His research interests include database and information systems, personalization and social networks, data infrastructures and digital libraries & repositories, scientific systems and workflows, eHealth systems, and human-computer interaction, topics on which he has published over one hundred articles in leading journals and conferences. He also holds three patents.

Harry Dimitropoulos, Dr, MIET (M), Research Associate, project manager and work package leader in Health-e-Child, Knowledge Discovery.

Manolis Tsangaris, Dr, (M), Research Associate, project manager and work package leader in Health-e-child, Medical Processing.

Metaxas Omiros, Dr, (M), Senior Researcher, Knowledge Discovery (Health-e-Child team member).

PARTNER 21: EMP - Empirica Gesellschaft für Kommunikations und

Technologieforschung MBH – SME - Germany


General description: Empirica is a leading European research institute in eHealth concepts, policies, strategic development, clinical and socio-economic impact, technology assessment and business development. Website: www.empirica.com

Expertise: Building on its expertise in the VPH area and through collaboration with the clinical, research and industrial communities. It regularly undertakes assessment and evaluation studies

http://www.empirica.com/

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of complex eHealth RTD developments and ICT-enabled systems and solutions applied to healthcare, including developing exploitation strategies and business plans, usually as international project leader. It has also undertaken research on eHealth strategy development and implementation by EU States, on market trends, including establishing the EC database of good practice case studies, and has performed foresight exercises. An acclaimed EU impact assessment of eHealth solutions (eHealth IMPACT) was published in 2006. EMP has strong experience in international cooperation in health IT policy, research and implementation, as well as global, trans-Atlantic and EU coordination. It has participated in EU-missions to the USA and Australia, and represented European research at global conferences.

Other participation to European projects and Networks of reference: VPHOP: The Osteoporotic Virtual Physiological Human; ARGOS : establishing a Transatlantic Observatory for Meeting Global Health Policy Challenges through ICT-Enabled Solutions; NMS PHYSIOME: Personalised models of the neuromusculoskeletal system; VPH-Share: Virtual Physiological Human: Sharing for Healthcare – A Research Environment; DISCIPULUS (leading partner): Digitally Integrated Scientific Data for Patients and Populations in User-Specified Simulations.

Role in the project: Empirica will lead WP19 and perform the health technology assessment, plan the exploitation strategy and contribute to the regulatory issues involed for medical usage of the developed technologies.

Key personnel

Karl A. Stroetmann, M.B.A., Ph.D., (M), received a doctorate in Business Administration and Economics from the University of British Columbia in Vancouver, Canada. He is Senior Research Fellow and head of the eHealth division of Empirica. He has been a consultant to EC, OECD, WHO, national governments and global eHealth industrial players. He is/was project coordinator or principal investigator in European projects on market validation and business development (various eTEN studies), economic impact assessment, on eHealth conceptual and policy issues. Presently he coordinates projects to support the implementation of the European eHealth Action Plan and to improve interoperability among Member State health systems. Karl is the main author of a WHO Policy Brief on Integrated Care and ICT enabled solutions.

Rainer Thiel, Ph.D., M.A., (M), obtained a doctorate and Master in Political Science from the Free University Berlin. He leads the research on clinical and socio-economic impact as well as technology assessment in the various VPH projects, and is responsible for business and exploitation planning as well as public policy and political system research. A member of Health Technology Assessment International (HTAi), the only international professional society focusing specifically on HTA, he has been designing assessment frameworks for the application to innovative and disruptive health technologies and ICT infostructures and plays a key role in the VPH-related NMS-Physiome and ARGOS projects. In a project for the EC DG Enterprise and Industry, he is task leader of the policy briefs on innovation policy governance.

Veli N. Stroetmann, MD, Ph.D., (F), obtained a doctorate in health informatics from the Bulgarian Academy of Medical Sciences and specialized and practiced as a Paediatrician. She is Principal Investigator to the VPHOP IP, involved in the NMS-Physiome Project (VPHOP-SIMBIOS [EU-USA] cooperation), and participates in the ARGOS project (EU-USA cooperation on Health Policy Challenges through ICT-Enabled Solutions, incl. VPH). She is Principal Investigator of the IP DebugIT which concentrates on patient data normalisation and integration for knowledge generation and decision support. ICT enabled patient safety solutions, health grid applications, EHR impact evaluation or semantic interoperability (with WHO involvement) were topics of other recent work.

Maria Smirnova, M.A., (F), received a Master degree in Health Management from the University of Applied Sciences in Krems, Austria, and a second Master degree in International Politics from the University of Manchester, UK. She is primarily involved in research roadmap design and socio-economic impact assessment pertaining to VPH-related projects and was responsible for

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the set up of the evaluation and exploitation framework in the context of VPH-Share.

PARTNER 22: Lynkeus - LYNKEUS – SME – ITALY


General description: Lynkeus is an independent strategy consultancy, founded in 2000, which works to identify and promote the best cutting-edge technological solutions to complex socio-economic problems in a variety of areas: ranging from eHealth, eGovernment, eProcurement and ePayment to asset-based welfare and voucher systems for social services. Partially a spin-off of two well-known Italian research centres: Censis, led by Prof. Giuseppe De Rita, and CER, led by the former Minister Giorgio Ruffolo, Lynkeus groups some of the most qualified researchers in the area of healthcare and technology analysis.

Expertise: Since 2005 Lynkeus has played leading roles in the conceptualisation, development and execution of major ICT for Health projects financed through the EC’s FP6 and 7. Starting with Health-e-Child (www.health-e-child.org) Lynkeus has been collaborating with leading European clinical, research and industrial institutions. This cooperation was expanded in 2010 with the launch of the Sim-e-Child (www.sim-e-child.org) project and the addition of John Hopkins University in Baltimore and the American College of Cardiology to a core research team composed of some of Health-e-Child’s partners. Besides playing a strategic role in supporting the coordination of these projects, Lynkeus has specialised in Project Management, Dissemination and Exploitation. In 2010 Lynkeus has been working with the Italian Region Emilia Romagna to analyse and trial a purchasing-card-based procurement system in healthcare, which is also being analysed at a European level in cooperation with the French “Association des professionnels de la carte d’achat (APECA)” and the Lisbon Regions Group, and in Italy by CartaSì, the Ministry of Economy and Finance, and the Bank of Italy. Formerly Lynkeus also played a key role in conceiving the MedChild Institute, organizing, for the Gerolamo Gaslini Foundation, the World Bank and the Arab Urban Development Institute, the Genoa Conference “Children and the Mediterranean”, and directed the development of “Charting the Mediterranean Child” and the “Child Well-Being Index”.

Other participation to European projects and Networks of reference : Besides the FP6 Health-e-Child and FP7 Sim-e-Child funded projects, Lynkeus has been a partner in the 2010 STREP Therapaedia proposal for ICT call 6 (FP7-ICT-2009.5.3), which got the excellent score of 14 out of 15, but no funding, because cardiology was not a priority, and has a been one of the 6 core partners in the 2011 FET Flagship proposal ITSME2, which didn’t succeed.

Role in the project: In MD-Paedigree Lynkeus is in charge of Project Management, supporting the Coordinator, leads the Dissemination & Training WP, and the Exploitation Task within the Exploitation, HTA and Medical Clearance WP. Lynkeus former role in Health-e-Child and in Sim-e-Child have involved it in being one of the founding partners of MD-Paedigree.

Key personnel

Edwin Morley-Fletcher, Prof, (M), is President of Lynkeus and Professor of Administration Science within the Faculty of Politics, University of Rome “La Sapienza”. He was a member of the National Council for Economy and Labour (CNEL) and chairman of the CNEL Working Group on the Social Market from 1995 to 2000 and a Senior Fellow at the School of Public Policy at the UCLA and former Jean Monnet Fellow at the EUI. Member of the Scientific Committee of the MedChild Foundation, he has been for several years advisor of the Gaslini Foundation, and has led the “Healthcare governance and technology” working group within the ASTRID Foundation. He operated as Project Manager within the FP6 Health-e-Child project (2006-20010) in the FP7 Sim-e-Child project (2010-2012). He’s the author of over 100 publications, several of which are

http://www.health-e-child.org/

http://www.sim-e-child.org/

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dedicated to eHealth and healthcare policy.

Alessandro Sattanino, PhD, (M), is an Economist with specific expertise in information science. Senior Manager at CER, in charge of numerous studies on the impact of the spread of ICT in the Italian Society and in government, he was. from 1996 to 2001 the IT Manager of the government Agency in charge of the Millennium in Rome. Formerly Professor of Economic Policy at the University of Molise, he is now Senior Economist and Board Member of Lynkeus.

Callum MacGregor, (M), MPhil Computer Speech & Language Processing, BSc (Hons) Mathematics & Artificial Intelligence, is Lynkeus Technical Architect.

Paolo Pavone, Prof, M.D, (M), is the Chair of the Lynkeus clinical oversight board. Director of the Department of Radiology at the “Mater Dei” Hospital in Rome and former head of the Department of Radiology at the University of Parma, he is a member of the Italian Society of Medical Radiology (SIRM), the Radiological Society of North America (RSNA), the Society of Magnetic Resonance Imaging (SMRI), the Cardiovascular and Interventional Radiological Society of Europe (CIRSE), the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB). He is the author or co-author of several hundred publications and organised or co-organised over 65 Italian and international clinical conferences.

PARTNER 23: SHC “SIEMENS Healthcare GmbH ”– “INDUSTRY” – “GERMANY”


General description: Siemens Healthcare is one of the world’s largest suppliers of technology to the healthcare industry and a leader in medical imaging, laboratory diagnostics, and clinical IT. With around 43,000 employees worldwide and a presence throughout the world, Siemens Healthcare generated revenue worth 11.7 billion euros and profits of more than 2 billion euros in fiscal 2014.

The healthcare market is in transition. Successful healthcare providers drive consolidation, an increasingly “industrial” logic of healthcare delivery, and a shift from treating illness to managing health. In these times of change, Siemens Healthcare aspires to become the trusted partner of healthcare providers worldwide – the partner that they can rely on to help drive their clinical excellence, operational efficiency, and profitability. To live up to this ambition, Siemens Healthcare is committed to further developing our traditional strengths in the imaging and laboratory fields and to complementing them with new offerings. Siemens Healthcare will continuously expand its management, consulting, and digital services, and broaden its portfolio e.g. with advanced therapy solutions and molecular in-vitro diagnostic products.

Siemens Healthcare is building on over 160 years of innovation and considers research and development (R&D) as the key driving force behind the innovations that safeguard the future of Siemens Healthcare. The Siemens Healthcare Technology Center works hand-in-hand with the R&D teams throughout the company and is positioned to be a multiplier for the Siemens Healthcare business areas. With major research centers in Germany, the U.S. and India TC conducts basic and applied research and develops and sustains technical and clinical research collaborations.

Expertise: The participating Image Analytics Research Group within the Siemens Healthcare Technology Center consists of 10 researchers and conducts R&D with particular focus on machine learning, object and pattern recognition, biophysical simulations, medical decision support systems, and visualization. His image analytics technology has been transferred to several key products of Siemens Healthcare.

Facilities: SHC will use standard PCs with modern graphics hardware to develop software for

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the project. Sag is equipped with multiple data servers to store large quantities of data (as medical images) and a fast network connection.

Other participation to European projects and Networks of reference: The Biomedical Informatics Research Program was involved in the EU project Health-e-Child and is currently the Project Coordinator of the EU-funded FP7 project Sim-e-Child. VPH models developed in both projects will be re-used and extended within the proposed project. The group is also involved in the FP7 project CARDIOPROOF investigating the cost effectiveness of physiological modeling for aortic diseases. In addition, the group led the THESEUS-MEDICO research consortium on semantic web technologies in healthcare, funded by the German Ministry of Economy. Semantics technologies originating from this project will be leveraged for the VPH infostructure development.

Role in the project: Within the proposed project, Siemens AG will contribute to both, the infostructure development and the VPH model development, mainly focusing on the cardiology and neuro-muscular disease areas

Key personnel

Olivier Ecabert, PhD, (M) Head of the Image Analytics Research Group. Before this position, Dr. Ecabert was Innovation Manager (2010-2013) in the Innovation department of the Angiography and Interventional X-ray Systems business unit of Siemens Healthcare working at the interface between research transfer and clinical evaluation. Dr. Ecabert has 12+ years of experience in medical image processing in the industry covering 3D model-based segmentation of cardiac images and interventional imaging. The results of his work were transferred to several innovative products for the diagnosis and treatment of cardiovascular disease. Dr. Ecabert was also overall Ecabert, project coordinator of a large scale European project from 2008-2010 (euHeart project, 17 institutions, €19M overall budget).

Maria Costa, PhD, (F) Research Scientist, joined Siemens in 2008. She specialized in 3D medical image segmentation using deformable models, and since then her participation in several EU projects has focused on automatic segmentation and registration, as well as on decision support and similarity search mostly in oncological scenarios. Within MD-Paedigree she is in charge of 3D image processing and segmentation for SAG.

Tobias Heimann, PhD, (M), recently joined Siemens as Research Scientist. From 2003 to 2008, he worked on automated medical image segmentation using 3D statistical shape models for his PhD at the University of Heidelberg. After that, he joined the EU Marie Curie RTN “3D Anatomical Human” as experienced researcher for biomechanical modelling of soft tissue. From 2010 to 2012, he led the segmentation team at the Div. Medical and Biological Informatics of the German Cancer Research Center. His main interests are automated image analysis, validation of segmentation algorithms and biomechanical modelling. His role in MD - Paedigree is to lead and supervise the image analysis tasks for SAG.

Olivier Pauly, PhD (M), Machine Learning Expert, has been Research Scientist with Siemens since 2014. He focuses on deep learning-based approaches for the detection of lesions in the context of breast cancer as well as in the context of metastatic bone cancer. From 2008 to 2013, he did his PhD and PostDoc at the Technische Universität München in Prof. Nassir Navab’s Computer Aided Medical Procedures group focussing on machine learning technologies such as random forests applied to medical image analysis and computer vision. In collaboration with Microsoft Research Cambridge, he participated to research projects addressing the tasks of automatic organ localization and segmentation in CT and MR images. Moreover he developed approaches for lesion detection in the context of early diagnosis of Parkinson disease. From 2013 to 2014, he joined the company Definiens AG as an application developer, where he worked on novel learning-based approaches for the analysis of digital pathology images.

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B.2.3 Consortium as a whole

In an attempt to try to limit any potential technical conflicts and to ensure the centralisation of the project’s decision making, the MD-Paedigree consortium has been designed to be comprised of partners with complementing skills and expertise with only limited technological or scientific overlap. The technology-focused partners have specific skills which address the tasks of the project for which they are responsible. The Coordinator and the other clinical partners are committed to provide not only excellence in paediatrics for the selected disease areas, but shall oversee the technological activities of the project, in order to guarantee the necessary complex clinical and technological integration, thus ensuring wide applicability and acceptance of the projects’ results.

The MD-Paedigree consortium is made up of twenty-two partners, eight of which were members of the Health-e- Child consortium and five have been working together within the Sim-e-Child project. Several partners within the MD-Paedigree consortium have also been cooperating through VPHNoE and/or through the VPH Institute, and will now enjoy the support of VPH-Share. Some clinical partners have been working together in Health projects, like for instance IGG and UMC Utrecht through the Marie Curie Action EUTRAIN (EUropean TRanslational training for Autoimmunity & Immune manipulation Network) or VUmc, KU-Leuven and OPBG, through ESMAC (European Society of Movement Analysis in Adults and Children).

These densely knit interwoven relationships cut across the different operational areas of MD-Paedigree and provide significant economies of scale, thus reducing the modelling costs for the different study areas of the project, and ensure the strong likelihood of eventually progressing beyond the state-of-the-art. Furthermore, the possibility of accruing the efforts realized in both Health-e-Child and Sim-e-Child on the field of repository implementation, together with the semantic tools developed in those projects, combined with the possibility of building on OPBG’s new Paediatric Cardiac Digital Repository which is underway in cooperation with maat G and Lynkeus, represent a consistent asset facilitating the attainment of the ambitious goals of MD-Paedigree infostructure.

All the partners have therefore already demonstrated effective expertise in complex interdisciplinary research projects and have a deep insight of the multi-scale computational models to be completed and integrated into MD-Paedigree’s infostructure. They are ready to make key contributions to develop the highly innovative healthcare platform and digital repository for European paediatrics MD-Paedigree is aiming at: they are committed to jointly provide the vertical integration of biomedical data, information and knowledge spanning the entire spectrum from genetic to metagenomic to clinical,

focusing for modelling and simulation on multi-scale image analytics and gait analysis,

enhancing the similarity search capabilities already developed in the past,

making use of information fusion, data mining and semantic tools,

to achieve the model-driven and database-guided biomedical decision support system and knowledge discovery infostructure.

The organisational innovation of having a clinical leadership for MD-Paedigree is a key for ensuring contextual problem-solving capacity and effective implementation and acceptance of the project’s outcomes together with the possibility of triggering an ongoing validation process exerted on a continuously growing database. This innovative structure is relying on being supported by a coordination and management team which includes partners with proven experience of proactive cooperation, as shown in particular in the cases of Health-e-Child and Sim-e-Child. The experience gained from these two projects, as well as from other occasions for VPH cooperation, has resulted in strong personal and professional ties between the key members of the Management and Technical Coordination Board, and the development of a well-oiled and adaptable infrastructure able to internally solve problems, mitigate risks, adequately monitor and steer the project’s development.

Each technical partner has been chosen because of specific qualities which they bring to the project in terms of excellence in their field, ehealth and VPH expertise and international research experience and exposure. Every conceptual action of MD-Paedigree involves the best experience in the research, clinical and industrial field for each implementation area, and a number of cooperation’s opportunities

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are planned.

B.2.3.1 Brief description of technical partners’ skills and expected cooperation between them for each specific action

Maat G, Hes-So, La Sapienza, ATHENA, USFD, constitute a strongly interconnected technical team for the development of MD-Paedigree infostructure, and the participation of Lynkeus in this action is finalised to a commitment towards facilitating exploitation and the future involvement of a growing number of clinical institutions. In this framework, the participation of Siemens in A3 is particularly significant and of special importance, given its advanced expertise in the Medico project within the Theseus programme. Funded by Germany’s Ministry of Economics and Technology, this programme aims at making information content available and understandable to computers, allowing them to develop a methodology ensuring order and hierarchy even when working with unstructured data.

With an approach similar to the conceptual thread started with Health-e-Child, Medico’s goal is therefore relatively close to one layer of MD-Paedigree. The latter, however, complements this layer with specific VPH interoperability and modelling and simulation functions, together with the ambition to trigger an ongoing model-and-data driven validation and engineering system, entirely focused on paediatrics.

Medico’s goal is in fact to bring together all the available medical imaging data for a specific patient, while also incorporating information from other patients with similar conditions, and to combine medical knowledge with new image-processing methods, knowledge-based information processing techniques, and machine learning technologies. Medico thus allows the system to autonomously interpret images of anatomical structures and recognize abnormal changes to them, while automatically cataloguing the data and linking them with reference images and treatment reports from several databases in order to restrict the semantic gap in a predefined area between unstructured image data and medical terminology.

An additional major advantage of MD-Paedigree includes its versatility, with software components being laid-out in a scene graph, and its ability to speed-up computation using GPU while coping with topological changes. Through compatibility with GPU processing, MD-Paedigree allows model-driven prediction and simulation to be locally executed onto live patient data, thus providing time critical support to physicians at the point of care. On the other hand, MD-Paedigree integrates an open Cloud API to its abstraction layer thus allowing its infrastructure to elastically adapt according to faced requests from end-users.

Besides guaranteeing active participation in all the modelling and infostructure European and international occasions for disseminating MD-Paedigree’s work and for clustering with other relevant R&D projects, Dissemination and Training are going to be partially intertwined, relying on the proven efficacy of the provision of ad hoc Dissemination Objects by Lynkeus (see the Health-e-Child Story videos as well as Sim-e-Child/Health-e-Child - New frontiers of imaging the human body on YouTube and UCL’s proven skills in training through Vanessa Diaz’ experience as PI in Discipulus and in the Marie Curie Initial Training Network in cardiovascular engineering and medical devices.

Exploitation, HTA and Medical Clearance are an important part of the project, and the ad hoc WP is led by such an experienced partner as Empirica.

On the basis of all these elements, the consortium partners believe that their composition represents a good balance between clinical, industrial, technical and academic, to address the challenges implied by making steps towards the development of a highly innovative model-driven European paediatric digital repository readily available at the point of care, with the ultimate goal of offering a sustainable translational service to healthcare professionals.

B.2.3.2 Subcontract

Subcontracting is foreseen for the audit certificates as required by the European Commission in the frame of the financial reporting and are allocated under management costs for a total of € 57,000.

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Partner 1 (OPBG) will allocated activities (catering, preparation and printing of project’s brochures, ecc.) for a total of € 20,000, in support of the project’s meetings, of the reviews and of the final closing event, as well as € 5.000 for translation of documents for ethical committee. Subcontractors will be chosen, when possible, among the Institution’s regular providers. External providers will be chosen based on EU selection criteria.

B.2.3.3 Third parties Not applicable

B.2.4 Resources to be committed

The MD-Paedigree IP will be a “cost-sharing” project, with costs being shared between its partners and the EC. Thus, each partner involved will pay costs of its own in order to participate in the project. The average level of cost-sharing is 27.8 %.

In order to increase the project’s strength, the consortium will guarantee additional unfunded effort for the project’s activities, for a total of 68 person/months and over € 122.500 of equipment.

The project is due to last for forty eight months. The project has been designed so that the work and the associated funding will be distributed uniformly over the duration of the project. Based on their known rates direct personnel costs and overheads have been calculated for each partner.

B.2.4.1 Use of resources

Following is a comprehensive picture of the project required resources, described both in terms of activities and of cost items.

B.2.4.1.1 Distribution of resources by activity

Coordination and Management: coordination and management activities (WP1) will employ a total of 122 person/months and a funding of € 787,615, corresponding to 6.64 % of the total requested EC contributioneffort.

RTD: Research and Technical Development (WP 2 – 16) will employ a total of 1,545 person/months corresponding to 83.7% of total effort. RTD activies are more specifically composed by the following 3 activity types:

Clinical: clinical activities (WP 2-7) will be allocated a total of 579.84 person/month € 2,644,628 corresponding to 22.2837.53 % of the total requested fundingeffort for RTD and 31.41 % of the total effort.

Modelling: modelling activities (WP8-12) will absorb € 3,932,642 622.05 person/month which represents 33.13 40.26 % of the requested effortfunding for RTD and 33.70 % of the total effort.

Infostructure: the VPH infostructure implementation (WP13-16) will reach use € 2,597,759 343.11 person/month corresponding to 21.8922.21 % of the requested effort for RTD and 18.59 % of the total effortfunding.

Demonstration: Demonstration actities (WP17) will employ 11.5 person/month corresponding to 0.70 % of the total effort.

Other: Dissemination and exploitation, including Health Technology Assessment activities (WP 18-19): will employ 67.5 € 1.906,356person/month , corresponding to 16.06 % of funding.9 % of the total effort.

Figure 18 shows the budget distribution among the project’s activities.

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Figure 18: Functional allocation of resources

B.2.4.1.2 Distribution of resources by cost item

The detailed distribution of cost items among total project costs can be summarised as follows:

Personnel: as shown in the WT6 table of the part A (“Project effort (in person/months) by beneficiary per work package”), over the project’s lifetime the consortium will deploy a total of 1,846 person/months worth of work.

Other costs

Other costs, account for € 1,097,58014,932, corresponding to 6.68 % of total costs, and are divided in the specific cost items:

– Equipment: equipment costs are € 334,703 corresponding to 30,49% of the total costs for OTHER. 32.35 % of equipment costs is unfunded as clinical partners will make available equipment they already own. These costs are limited also thanks to the involvement of some partners in the Health-e-Child and Sim-e-Child projects, in which they already invested in the necessary equipment.

– Consumables: costs related to consumables also are limited, for a total of € 284,521 corresponding to 25,92 % of the total costs for OTHER. Consumables mostly are represented by reagents for the different clinical and genetic analysis.

– Travel and meetings: costs are € 409,656, accounting for 37,32 % of the total costs for OTHER, and are mainly for partner’s participations to the project’s meetings.

– Publications: costs for publications correspond to a total of € 33,200 corresponding to 3,02 % of total costs for OTHER.

– Other: costs are € 35,500, accounting for 3,23 % of the total costs for OTHER, and are mainly used for organization an support to events related to final event.

Subcontracting: activities subcontracted have a total cost of € 82,000 corresponding to 0.5 % of total costs. These are represented by:

– external audit certificates, as requested by the EC financial guidelines, for a total of € 57,000;

– organisation of project’s meetings, reviews and events, and translation of documents for a total of € 25,000.

22,22%

33,17%

21,89%

6,59%

12,22%

3,91%Clinical background activities

Modelling and simulation

Infostructure

Coordination & management

Dissemination and exploitation

Health technology assessment & medical clearance

33,13 %

22,28 % 3,91 %

12,15 %

6,64 %

21,89 %

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The internal distribution of cost items among other direct costs is as follows:

Figure 19: Distribution of other direct costs

B.2.4.1.3 Use of resources for each partner

Each partner’s detailed budget, and the explanation of its own resources made available to the project, are shown in the following pages.

32,88%

27,43%

33,35%

3,26% 3,08%

Equipment

Consumables

Travel/meetings

Publications

Other

30.49 %

37.32 %

3.02 % 3.23 %

25.92 %

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Partner: 1 Ospedale Pediatrico Bambino Gesù ( OPBG )

The Partner 1 is leader of WPs 1,2,3,7,12, and takes part to WPs 4,5,6,8,9,10,11,13, 18, 19

Table 3: detailed budget distribution

OPBG RTD MGT OTH TOTAL

Personnel 1.012.165 110000 65000 1.187.165

Subcontracting 35.000 35.000

Other 152.622 45.000 4.000 201.622

Equipment 30.000 30.000

Consumables 52.622 52.622

Travel/meetings 60.000 15000 4000 79.000

Publications 10.000 10.000

Data analysis 0

Other 30000 30.000

Total Direct costs 1.164.787 190.000 69.000 1.423.787

Indirect costs 432.136 57.505 25.599 515.240

Total Cost 1.596.923 247.505 94.599 1.939.027

Requested EC contribution

1.073.276 247.505 94.599 1.415.369

Detailed explanation of each cost item and of the partner’s own contribution

a) Partner’s own staff: coordinator - Bruno Dallapiccola; clinicians - Giacomo Pongiglione, Melania Manco, Enrico Bertini, Enrico Castelli, Fabrizio de Benedetti, Paolo Tomà, Lorenza Putignani; Grant Office staff for management activity: Sonya Martin, Riccardo Bosco, Nicola Bergonzi.

b) Project staff involved in the project: PhD students, biologists and technicians.

c) Subcontracting: the cost is represented by a) audit certificates; b) support for project meeting’s organization: catering, development and printing of specific brochures and leaflets, ecc; translation of documents for ethical commitee.

d) Equipment: 2 freezers -80° C for biological samples storage.

e) Consumables: consumables are mainly autoantibodies for cytometry analysis, plasticware, farefor cytometry, hand held microFET2, C-reactive protein, Tumor-Necrosis Factor-a, TNF-a, Interlukin 6, IL5, LPS, Adiponectin, Leptin.

f) Travel/meetings: this category of cost includes participation to the kick-off meeting, to the periodic and final project meetings. Moreover it includes travels for dissemination events and “user requirements” activities.

g) Publications: manuscript submission and publication fees.

h) Other: support to final dissemination event rental of location, press releases, social dinner, travel and accommodation for expertexternal speakers, dissemination gadgets.

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Partner: 2 University College London (UCL)

The Partner 2 is leader of WP 4, and takes part to WPs 2,3,7,8,9,12, 18, 19


The detailed explanation of each cost item and of the partner’s own contribution is as follows:

a) Partner’s own staff: Andrew Taylor and Vanessa Diaz

b) Project staff: clinical research fellow and research assistant for WP 3 and 4 to cover patient consent, acquisition of clinical data, imaging data and other assessment data and to ensure quality of data sent to other partners for analysis. Throughout the project, new methods of imaging assessment will be developed.

c) Subcontracting: by audit certificate

d) Consumables: consumables for acquisition of all clinical data, imaging data (except MRI) and all assessment data that will be acquired as part of routine clinical practice, or funded by infrastructure from the Centre or cardiovascular imaging and the Clinical Research Unit.

e) Travel/meetings: this category of cost includes participation to the kick-off meeting, to the periodic and final project meetings as well as to major dissemination events.

UCL RTD MGT OTH TOTAL

Personnel 473.988 94.508 568.496


Other 30.899 30.899

Equipment


Travel/meetings 10.000 10.000

Publications

Other

Total Direct costs 504.887 2.500 94.508 601.895

Indirect costs 302.932 56.704 359.636

Total Cost 807.819 2.500 151.212 961.531

Requested EC contribution 571.253 2.500 151.212 724.965

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Partner: 3 Istituto Giannina Gaslini (IGG)

The Partner 3 is leader of WP 5, and takes part to WPs 2,7,10,12,18, 19



a) Partner’s own staff: Alberto Martini, Clara Malattia, Marco Gattorno, Paolo Moretti, Gian Michele Magnani.

b) Project staff: post-docs and residents to be appointed over the four year project dedicated to clinical, imaging and laboratory data collection and follow-up.

c) Consumables: laboratory reagents and kits for in vitro studies.

d) Travel/meetings: this category of cost includes participation to the kick-off meeting, to the periodic and final project meetings as well as to major dissemination events.

e) Publications: manuscript submission and publication fees.

IGG RTD MGT OTH TOTAL

Personnel 224.100 18.000 242.100

Subcontracting

Other 5.000 13.000 18.000

Equipment




Other

Total Direct costs 229.100 31.000 260.100

Indirect costs 137.460 18.600 156.060

Total Cost 366.560 49.600 416.160

Requested EC contribution 258.360 49.600 307.960

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Partner 4: DHZB – Deutsches Herzzentrum Berlin

The Partner 4 takes part to WPs 3,4,7,8,12



a) partners own staff: Alireza Khasheei, Nady Al-Wakeel, Felipe Gonzales.

b) Project staff: Research fellow and technician appointed for the duration of the project.

c) Travel/meetings: this category of cost includes participation to the periodic and final project meetings.

DBHZ RTD MGT OTH TOTAL

Personnel 107.466 107.466

Subcontracting

Other 0 4.000 4.000

Equipment

Consumables 0 0


Publications 0 0

Other


Indirect costs 64.480 2.400 66.880

Total Cost 171.946 6.400 178.346

Requested EC contribution 128.956

6.400

135.356

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Partner: 5 Katholieke Universiteit Leuven (KU Leuven)

The Partner 5 takes part to WPs 2,6,11,12,18, 19


KU LEUVEN RTD MGT OTH TOTAL

Personnel 211.818 8.000 219.818

Subcontracting

Other 18.155 5.845 24.000

Equipment


Travel/meetings 10.155 5.845 16.000

Publications

Data analysis

Other


Indirect costs 137.984 8.307 146.291

Total Cost 367.957 22.152 390.109



a) Partner’s own staff: Kaat Desloovere

b) Project staff: PhD student for RTD activity, and post-doc for Other activity.

c) Consumables: gait analysis data, including markers, tape EMG electrodes; PC for data analysis, license software SAS/Matlab; Scanning costs for medical imaging.


Formattato: Colore carattere: Rosso



























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Partner: 6 Stichting VU-VUMC (VUmc)

The Partner 6 is leader of WP 6, and takes part to WPs 2,11,12,18, 19


VUMC RTD MGT OTH TOTAL

Personnel

223.878

23.000

246.878

Subcontracting

-

Other

-

8.000

8.000

Equipment

-

Consumables

-

Travel/meetings

8.000

8.000

Publications

-

Data analysis

-

Other

-

Total Direct costs

223.878

-

-

31.000

254.878

Indirect costs

134.327

-

-

18.600

152.927

Total Cost

358.205

-

49.600

407.805


253.390

-

49.600

302.990


a) Partner’s own staff: Jaap Harlaar

b) Project staff: PhD student

c) Travel/meetings: this category of cost includes participation to the kick-off meeting, to the periodic and final project meetings as well as to major dissemination events.

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Partner: 7 Universitair Medish Centrum Utrecht (UMC Utrecht)

The Partner 7 takes part to WPs 2,5,7,10,12, 18, 19



a) Partner’s own staff: B. Prakken, N. Wulfraat, N. de Jager, J. Meerdink, R. Scholman.

b) Project staff: technician, junior PI, paediatric rheumatologist.

c) Consumables: antibodies Luminex, beads and laboratory reagents.


UMC UTRECHT RTD MGT OTH TOTAL

Personnel 153.100 23.000 176.100

Subcontracting

Other 54.000 8.000 62.000

Equipment



Publications

Other


Indirect costs 124.260 18.600 142.860

Total Cost 331.360 49.600 380.960


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Partner: 8 Siemens AG (SAG)

Partner 8 is, until April 30th, 2015, is leader of WPs 8 and 9 and takes part to WPs 1, 2, 3, 4, 6, 11, 12, 13, 14, 15 and, 16, 17, 18 and 19.


SAG RTD MGT OTH TOTAL

Personnel 705.743 5.423 711.166


Other

Equipment

Consumables

Travel/meetings

Publications

Data analysis

Other


Indirect costs 568.033 4.515 572.548

Total Cost 1.273.776 11.477 1.285.253



a) Partner’s own staff: Olivier Ecabert, Tobias Heiman, Maria Costa and Michael Suehling, for project management and RTD in Activity 2 (modelling). The Other personnel costs are foreseen for training activity such as seminaries and workshop and to major dissemination events.

b) Project staff: research scientist full-time for RTD activity.

c) Subcontracting: audit certificate

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Partner: 9 Biomolecular Research Genomics Srl (BMR genomics)

The Partner 9 takes part to WP 7



a) Partner’s own staff: Barbara Simionati, laboratory staff.

b) Project staff: bio-informatics and Laboratory staff

c) Consumables: reagents for DNA sequencer, DNA purification kits, DNA quantification kits, SPRI magnetic bits.

d) Travel/meetings: this category of cost includes participation to the kick-off meeting, to the periodic and final project meetings.

BMR RTD MGT OTH TOTAL

Personnel 142.500 142.500

Subcontracting

Other 105.000 105.000

Equipment



Publications

Data analysis

Other

Total Direct costs 247.500 247.500

Indirect costs 148.500 148.500

Total Cost 396.000 396.000

Requested EC contribution 297.000 297.000

Cooperation

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Partner: 10 Fraunhofer Gesellschaft zur Foerderung (Fraunhofer)

The Partner 10 takes part to WPs 2,4,5,9,10, 12, 18, 19



a) Partner’s own staff: Stefan Wesarg, 2 qualified staff members.

b) Project staff: 1 senior researcher, 2 researcher associates, student workers.

c) Subcontracting: audit certificate.


UMC FRAUNHOFER RTD MGT OTH TOTAL

Personnel 363.399 23.000 386.399


Other 4.000 8.000 12.000

Equipment

Consumables


Publications

Other


Indirect costs 345.801 23.095 368.896

Total Cost 713.200 4.000 54.095 771.295


Cooperation

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Partner: 11 Institut Nationale de Recherche en Informatique et en Automatique (INRIA)

The Partner 11 takes part to WPs 3,4,8,9,18, 19



a) Partner’s own staff: Xavier Pennec, H. Delingette, M. Sermesant

b) Project staff: PhD Students


d) Travel/meetings: this category of cost including participation to kick-off meeting, periodic and final meetings. Moreover it includes annual conference MICCAI, ISBI; conference every 2 years IPMI. Participation to major dissemination events, and in particular STACOM, STIA and MFCA, MMBIA

INRIA RTD MGT OTH TOTAL

Personnel 209.053 19.000 228.053


Other 32.000 8.000 40.000

Equipment

Consumables


Publications

Other


Indirect costs 248.285 27.457 275.742

Total Cost 489.338 1.500 54.457 545.295


Cooperation

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Partner: 12 Motek Medical B.V. (MOTEK)

The Partner 12 is leader of WP 11, and takes part to WPs 2,5,6,10,12, 18


MOTEK RTD MGT OTH TOTAL

Personnel 534.333 26000 560.333


Other 220.000 0 8.000 228.000

Equipment 220.000 220.000

Consumables 0

Travel/meetings 8000 8.000

Publications 0

Data analysis 0

Other 0


Indirect costs 150.867 0 6.800 157.667

Total Cost 905.200 4.000 40.800 950.000


561.000 4.000 40.800 605.800


a) Partner’s own staff: Frans Steenbrick, Oshri Even Zohar (CTO), Thomas Geijtenbeek, Andre Prins.

b) Project staff: PhD Students and Sanne Roeles (developer).


d) Equipment: dual-belt instrumented treadmill and 8 camera’s motioncapture system, to capture kinematic and kinetic gait data, needed to run the Human Body Model, test the developed models and develop the speed-dependend models based on our D-Flow software. Partner’s own motion-base and related motion-capture system will be used for in depth investigation of perturbations and modelling of subject responses.


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Partner: 13 Siemens Corporation (SCR)

The Partner 13 takes part to WPs 2,8,9,18



a) Partner’s own staff: Managers Siemens Corporation.

b) Project staff: PhD Students (Tommaso Mansi, Puneet Sharma and Bogdon Georgescu).

c) Travel/meetings: this category of cost includes participation to the kick-off meeting, to the periodic and final project meetings.

SCR RTD MGT OTH TOTAL

Personnel 198.462 3.141 201.603

Subcontracting

Other 4.656 4.656

Equipment

Consumables


Publications

Other


Indirect costs 87.882 1.359 89.241

Total Cost 291.000 4.500 295.500


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Partner: 14 Technische Universiteit Delft (TU Delft)

The Partner 14 takes part to WPs 11,18, 19


TUD Delft RTD MGT OTH TOTAL

Personnel

121.669

10.000

131.669

Subcontracting

-

Other

17.000

-

-

-

17.000

Equipment

-

Consumables

8.500

8.500

Travel/meetings

8.500

8.500

Publications

-

Data analysis

-

Other

-

Total Direct costs

138.669

-

-

10.000

148.669

Indirect costs

88.891

-

-

6.410

95.301

Total Cost

227.560

-

16.410

243.970


170.670

-

16.410

187.080


a) Partner’s own staff: Frans Van der Helm.

b) Project staff: PhD Students.

c) Consumables: laboratory reagents and kits.


Cooperation

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Partner: 15 Università degli Studi di Roma La Sapienza (URLS)

The Partner 15 take parts to WPs 5,6,10,11, 13,15,16,18,19


URLS RTD MGT OTH TOTAL

Personnel 280.867 25.000 305.867


Other 24.000 8.000 32.000

Equipment 0

Consumables 0



Data analysis 0

Other 0


Indirect costs 182.920 19.800 202.720

Total Cost 487.787 4.000 52.800 544.587


344.240 4.000 52.800 401.040


a) Partner’s own staff: Paolo Cappa, Fabrizio Patanè, Stefano Rossi.

b) Project staff: PhD Students and research associate.



e) Publications: manuscript submission and publication fees for papers and open access journals.

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Partner: 16 University of Sheffield (USFD)

The Partner 16 is leader of WP 10, and takes part to WPs 2,5,11,12,13,18,19


USFD RTD MGT OTH TOTAL

Personnel 560.326 32114 592.440


Other 171.000 0 0 171.000

Equipment 55.000 55.000




Data analysis 0

Other 0


Indirect costs 438.796 0 19.268 458.064

Total Cost 1.170.122 6.000 51.382 1.227.504


877.591 6.000 51.382 934.974


a) Partner’s own staff: Marco Viceconti, Rod Hose, Alejandro Frangi, A. Wood

b) Project staff: post-docs for musculoskeletal modeling, image modeling and processing, IT infrastructure/VPH-share-integration.


d) Equipment: computational equipment, workstations, processing or storage node for cluster for USFD Iceberg supercomputer and of the Insigned Cluster.

e) Consumables: imaging contrast agents, calibration phantom, mass storage media.

f) Travel/meetings: this category of cost includes participation to the kick-off meeting, to the periodic and final project meetings as well as to major dissemination events.

g) Publications: open access publication costs.

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Partner: 17 MAAT France (maat G)

The Partner 17 is leader of WP 14, and takes part to WP 1,2,13,15,17,18,19



a) Partner’s own staff: David Manset, Nicolas Mugnier and Baptiste Grenier.

b) Project staff: digital repository developer and expert in technology watch, dissemination and exploitation strategies.


d) Equipment: 2 development laptops.


maat G RTD DEMO MGT OTH TOTAL

Personnel 421.754 8.106 18.253 21.628 469.741


Other 18.503 12.000 30.503

Equipment 6.503 6.503

Consumables

Travel/meetings 12.000 12.000 24.000

Publications

Other

Total Direct costs 440.257 8.106 23.253 33.628 505.244

Indirect costs 264.154 4.863 10.951 20.176 300.144

Total Cost 704.411 12.969 34.204 53.804 805.388


528.308 6.484 34.204 53.804 622.800

Cooperation

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Partner: 18 Haute Ecole Specialisee de Suisse Occidentale (HES-SO)

The Partner 18 is leader of WPs 13,15; and takes part to WPs 2,14,16,17,18,19



a) Partner’s own staff: Henning Muller, Patrick Ruch.

b) Project staff: PhD students.



HES-SO RTD DEMO MGT OTH TOTAL

Personnel 458.056 18.944 25.000 502.000


Other 11.500 11.500

Equipment

Consumables


Publications

Other


Indirect costs 274.833 11.366 21.900 308.099

Total Cost 732.889 30.310 4.000 58.400 825.599

Requested EC contribution 524.845 15.155 4.000 58.400 602.400

Cooperation

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Partner: 19 Universitatea Transilvania Din Brasov (UTBV)

The Partner 19 takes part to WPs 14,16,17,18,19



a) Partner’s own staff: Professor, Associate, senior Lecturers.

b) Project staff: PhD students, support personnel.

c) Equipment: UTBV will make available GPU cluster for the project, and acquire a server, desktops and mobile devices managing/analyzing resulted information.

d) Consumables: specific project office materials.

e) Travel/meetings: this category of cost including participation of Kick-off meeting, periodic meeting and final meeting and to major dissemination events.

f) Publications: manuscript submission and publication fees.

UTBV RTD DEMO MGT OTH TOTAL

Personnel 146.818 3.363 23.000 173.181

Subcontracting

Other 37.400 8.000 45.400

Equipment 15.000 15.000




Other 2.400 1.500 3.900


Indirect costs 110.530 2.017 18.600 131.147

Total Cost 294.748 5.380 49.600 349.728


Cooperation

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Partner: 20 ATHENA Research and Innovation Center in Information Communication & Knowledge Technologies (ATHENA)

The Partner 20 is leader of WP 16 and of WP 17, and takes part to WPs 2,9,13,14,15,18,19



a) Partner’s own staff: Yannis Ioannidis.

b) Project staff: senior researchers and junior researchers.


d) Consumables: specific project office materials.


UoA RTD DEMO MGT OTH TOTAL

Personnel 454.440 21.060 21.500 497.000


Other 12.000 8.000 20.000




Publications

Other


Indirect costs 279.864 12.636 17.700 310.200

Total Cost 746.304 33.636 3.500 47.200 830.700

Requested EC contribution 535.152 16.848 3.500 47.200 602.700

ATHENA

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Partner: 21 EMPIRICA (EMP)

The Partner 21 is leader of WP 19 , and takes part to WP 18



a) Partner’s own staff: Karl A. Stroetmann.

b) Project staff: senior researchers, junior researchers and consultants.

c) Travel/meetings: this category of cost including participation of Kick-off meeting, periodic meeting and final meeting and to major dissemination events.

EMP RTD MGT OTH TOTAL

Personnel 220.000 220.000

Subcontracting

Other 8.000 8.000

Equipment

Consumables


Publications

Other

Total Direct costs 228.000 228.000

Indirect costs 136.800 136.800

Total Cost 364.800 364.800

Requested EC contribution 364.800 364.800

Cooperation

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Partner: 22 LYNKEUS (Lynkeus)

The Partner 22 is leader of WP 18 , and takes part into WPs 1,2,13,14,15,16,19



a) Partner’s own staff: Edwin Morley-Fletcher, IT manager.

b) Project staff: 1 senior manager, 2 junior researchers.


d) Equipment: dedicated PC and IT accessories.


f) Publications: newsletters and other dissemination material.

g) Other: website building and maintenance.

LYNKEUS RTD MGT OTH TOTAL

Personnel 103.232 298.000 373.782 775.014


Other 24.000 24.000


Consumables



Data analysis

Other 1.600 1.600


Indirect costs 46.444 134.100 179.002 359.546

Total Cost 149.676 440.100 576.784 1.166.560

Requested EC contribution 109.896 440.100 576.783 1.126.747

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Partner: 23 Siemens HealthCare GmbH (SHC) From May 1st, 2015, SHC has replaced SAG and is leader of WPs 8 and 9 and takes part to WPs 1, 8. 9, 11, 12, 18 and 19. Table 11: detailed budget distribution

SHC RTD DEMO MGT OTH TOTAL

Personnel 1.081.485 20.601 32.595 1.134.681

Subcontracting 3.000

Other Equipment

Consumables

Travel/meetings 19.200

Publications

Data analysis

Other

Total Direct costs

Indirect costs

Total Cost

1.100.685

23.601

32.595

1.156.881


547.343 23.601 32.595 603.539


a) Partner’s own staff: Olivier Ecabert, Tobias Heimann, Maria Costa, Olivier Pauly, Manasi Datar, Dominik Neumann, for project management and RTD in Activity 2 (modelling). The Other personnel costs are foreseen for training activity such as seminaries and workshop and to major dissemination events.

b) Project staff: research scientists part-time for RTD activity.

a)c) Subcontracting: audit certificate

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.3 Impact

B.3.1 strategic impact

If there is a growing social concern it is that our world is becoming increasingly unfair toward the younger generations. The generational divide is more or less pronounced in each member state, but overall it is a signature of the current globalised economy for all European countries. In many cases this is against the most common sense. Let us consider healthcare. The amount of research attention, care, and expenditure for the ageing population is quickly shadowing what we invest to improve the health of our children, in spite the fact that these are obviously the best spent money for our society. When the European Commission talks about Active and Healthy Ageing, it is important to recognise that, in order to age healthy, as a first step you have to be a healthy child.

On the other hand, diseases in infants, children, and adolescents are a large and under-appreciated public health problem. Because paediatric patients now have a much longer life expectancy, the burden and costs are substantial for families and society. For instance, recent studies have shown that the number of adult patients with congenital heart disease is already similar to that of the paediatric population and will continue to grow.

MD-Paedigree aims to advance the state-of-the-art of patient-specific computational modelling of different paediatric diseases and translate the latest advances into clinics to improve disease understanding, therapy outcome, and provide an infostructure platform for assessing new therapies at the point of care. By fostering a close cooperation among leading European and complementary American clinical and technological partners, MD-Paedigree can increase research excellence, which will result in augmented potential for improved healthcare solutions for children.

B.3.1.1 MD-Paedigree’s impact on listed targets

MD-Paedigree was designed to maximize the intended impacts of the call’s target outcome. Its capacity stems both from (i) MD-Paedigree’s contents, and (ii) from its nature of being a clinically-led ICT for health project. In particular, the project aims at the following strategic impact factors:

B.3.1.1.1 More predictive, individualised, effective and safer healthcare

It should be recognised that the paediatric application poses original and specific problems that make most of the VPH not specifically targeted to children only marginally useful.

MD-Paedigree completes and enhances existing disease models and data repositories deriving from former EC-funded research (especially the FP6 IP Health-e-Child and the FP7 STREP Sim-e-Child) and from industry and academia. Efficient access and exploitation of scattered and heterogeneous information is still a major challenge in today’s clinical routine work; integrating this data and making it accessible anywhere and anytime through a sustainable, web-based data and model repository is a crucial step towards more effective healthcare. Clinically driven requirements and modelling and corresponding thorough clinical validation studies are at the core of the project and render the multi-scale models robust and reproducible enabling their safe and efficient application to routine clinical cases.

In daily clinical practice, decisions about whether to perform a therapy, when to perform it and which patients will profit from the procedure remain challenging in all disease areas covered in this project. The multi-scale disease modelling and similarity search in MD-Paedigree will allow subdividing heterogeneous diseases into more homogenous sub-classes for which therapies will be tailored, rendering them more individualized and effective. In addition, comprehensive predictive simulations proven by their validation on longitudinal data will allow to evaluate different treatment regimes and rehabilitations in-silico to decide on the most effective and safest one to apply.

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By developing compatibility for GPU processing, and for model-driven prediction to be locally executed onto live patient data, thus providing time critical support to physicians at the point of care, MD-Paedigree enables clinicians to work faster, more easily and more cost-effectively, delivering better preventive and predictive healthcare.

By provisioning clinical trials within the developed infostructure, MD-Paedigree may also influence the assessment of therapeutic effects in time-consuming and extremely costly clinical trials for drug discovery, rendering them more effective. Means for earlier and more effective therapy assessment and predictive simulations may play a role for future pharmacological research and developments with significant impact on the healthcare market.

B.3.1.1.2 Reinforced leadership of European industry and strengthened multidisciplinary research

excellence in supporting innovative medical care

If there is a golden run for healthcare industry, this is that the market is where the clinical needs are. This is why in our opinion a project like MD-Peadigree, which is clinically led, has a much greater chance to produce technological research results of great impact on the healthcare industry of Europe.

By bringing together a unique combination of highly specialised clinical and technological consortium partners, MD-Paedigree increases research excellence, resulting in augmented potential for improved healthcare solutions for children. On the technical side, besides Siemens as one of the world's largest suppliers to the healthcare market and a trendsetter in medical imaging, laboratory diagnostics, and medical information technology, agile SME’s and excellent academic European centres will advance the development of more elaborate and reusable multi-scale models and information repositories including ICT tools, services and infrastructure that are necessary to apply the models in clinical practise. The infrastructure and tools will become part of novel and competitive medical equipment and hospital IT, not only markedly improving clinical outcomes, but also fostering the establishment of a European leadership in this specific area. By operating in close cooperation with clinical centres of excellence as prime users and early adopters, MD-Paedigree promotes a reinforced leadership of European industry in the area of paediatrics and more generally a strengthened multidisciplinary research excellence in supporting innovative medical care. MD-Paedigree also strengthens the small entrepreneurships involved in the project and may even foster the generation of new SME’s. Thus, MD-Paedigree has the potential to strengthen the leadership of European industries, both, at small and large scale.

The market rationale of MD-Paedigree is that already in the research phase, an “enticement” open access level business model should be considered, with various levels of usage, each implying differential costs. This way, any clinical institution accessing MD-Paedigree, would need, if willing to go beyond the open access level and make full use of the data-base and also of specific tools/applications available on MD-Paedigree, to enter a temporary subscription or compensate the service in some other form, according to the requested level of usage. The business logic should be a win-win one, somehow similar, in abstract terms, to how Apple deals economically with its application providers, according to predefined commercial agreements. In as much as MD-Paedigree will succeed in inducing a growing number of paediatric clinical institutions and technology developers to join in, by the same token will it also make it true that the more data can be brought together, the more robust and accurate the models will be.

B.3.1.1.3 Improved interoperability of biomedical information and knowledge

An integral part of MD-Paedigree is the integration of data and models into a sustainable VPH repository. Advanced knowledge representation structures such as biomedical ontologies and semantics will not only allow the interoperability between the incorporated information domains but also the seamless linkage of external knowledge to it. The repository is being conceived of as a specific part of the VPH Infostructure described in the ARGOS roadmap. Through ontology-based data access and scalable query execution built on open standards and protocols that capture all related biomedical information & data, bio-mechanical models, patient-/ disease-/ user-specific profiles, the platform provides services in

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order to deliver a complete and generic solution to the data and information access challenges posed by the paediatric biomedical community.

The close cooperation with the VPH-Share project, one of the two VPH infrastructure project, will ensure that what is developed in MD-Paedigree is highly interoperable with the rest of the VPH infostructure.

B.3.1.1.4 Increased acceptance and use of realistic and validated models that allow researchers from

different disciplines to exploit, share resources and develop new knowledge

At its core, MD-Paedigree integrates different disease areas and various research disciplines with the clear goal to exploit synergies and share resources between them and to discover new knowledge. Specific attention will be paid to ensure the interoperability of models through the re-use of models between the different disease areas where possible. In addition, the usage of open source software and environments will facilitate acceptance of the models by the VPH community. As an example, DICOM, the Digital Imaging and Communications in Medicine standard for interoperability between different systems, was first introduced in the early nineties – almost 30 years ago – and yet, even today, interoperability is still a major issue in eHealth. Only in 2011, on the other hand, the new DICOM Part 19 has been approved. This is a standard that provides the mechanism by which a Hosting System can launch or connect to one or more add-on Hosted Applications through the standardised Application Program Interface (API). It will play a significant role within MD-Paedigree, allowing it to exert a concrete impact on acceptance of the developed technology. Aiming at long-term continuity, MD-Paedigree shall ensure a long-term sustainable precondition for transformative research in ICT for health also by making use of this standard to provide a platform for dedicated applications. It will thus encourage further investment in European paediatric model-guided medicine, and place its participating clinical centres (through its spin-offs and ad hoc companies) at the forefront position globally in health delivery and research.

Furthermore, thanks specifically to its clinically-led nature, MD-Paedigree has a unique capacity of guaranteeing increased acceptance and use of realistic and validated models that allow clinicians and researchers, cooperating in a multi-disciplinary effort, to share resources, develop new knowledge, and exploit it.

Acceptance and use of VPH models is in fact a key factor which cannot be taken for granted: “no matter how well engineered a software solution is, its success (or failure) will be determined by its users (or lack thereof)”. With regard to the risk of final non-acceptance and non-use, however, MD-Paedigree’s integrative models are explicitly requested by the clinicians and constitute the basis for the new model-guided workflows to be applied in the routine activity of the participating clinical centres. This precondition is therefore structurally far from the often encountered situation by which “in many, if not most, instances new system technologies do not necessarily imply a win-win situation for all participants, but rather that some of them may lose, and (if they have a powerful position within the healthcare system) may even act as so-called ‘veto players’ being able to block innovations in spite of their overall convincing return to society”.

If it is true that “it is to be expected that highly disruptive VPH technologies may fall into such a category, requiring complex interventions at the health system level and compensation for those who otherwise may be able to block diffusion”, MD-Paedigree can counter having healthcare centres of excellence as prime users through its models and data digital repository, jointly committed to attain a goal-driven, federated effort towards a challenging scientific and technological vision to ensure European paediatric leadership in VPH.

All through the proposal there is an evident tension toward clinical pathways that minimise the organisational impact and the patient’s discomfort, and not those that make it easier to generate predictive models. Keeping an adult 45 minutes still inside an MRI gantry is difficult; keeping a child is simply impossible. This is to say that the paediatric application poses severe limitations to what we are allowed to do to our patients: in MD-Paedigree this is translated into a grand research challenge that cuts transversally the entire research program.

Since changing organisational structures and culture, work processes and behaviour are among the

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most difficult tasks to accomplish in making any improvement to health and social care service delivery, MD-Paedigree ambitions to be a tool to instigate, facilitate and promote organisational change and improvements, and to lead to health professionals’ ability to respond to the paediatric needs and increasing expectations to provide robust and reusable integrated and validated patient-specific multi-scale computational models with high predictive power.

B.3.1.1.5 Accessibility to existing knowledge by bio-medical researchers through the VPH repositories

linking data with models will prove the large-scale benefits of having both the data and models readily

available

MD-Paedigree encompasses complete services for storage, similarity search, outcome analysis, risk stratification, and personalised decision support in paediatrics within its innovative model-driven data and workflow-based models repository, leveraging on service and knowledge utilities. It fosters the state-of-the-art of patient-specific computational modelling of the selected diseases and translates the latest advances into clinics to improve disease understanding and provide a platform for testing new therapies at the point of care.

In fact, MD-Paedigree demonstrates how a dedicated VPH repository can provide full accessibility to existing and further developing knowledge in paediatrics to all interested bio-medical researchers precisely through linking data with models, thus proving the large scale benefits of having both the data and models readily available at the point of care.

B.3.1.1.6 Steps needed to realise impact

MD-Paedigree’s leadership will promote a constant and dynamic interplay between its academic, clinical, and industrial participants to ensure that new technologies are developed with a very clear view toward translation and to guarantee continuous information exchange. To ensure that this interdisciplinarity is effectively exploited and that the steps necessary to achieve this impact are performed, the consortium will put in place a cutting edge communication and management system such as virtual meeting rooms that permits all the stakeholders to actively and routinely interact easily and effectively. At project start, a self-assessment, quality assurance guidelines and a comprehensive risk analysis taking into account among others assumptions and external factors together with corresponding risk mitigation plans will help MD-Paedigree to achieve the intended impact.

B.3.1.1.7 Need for a European rather than a national approach

A fundamental challenge in the field of comprehensive disease modelling is the need for interdisciplinarity, ranging from clinical expertise, imaging, molecular analysis, biomechanics to knowledge discovery. A European approach is absolutely necessary to achieve the interdisciplinarity required to make this project a success, including its translation to market impact. The MD-Paedigree partners have been selected to provide the necessary first-class and complementary expertise required to address this complex task. With the inclusion of Siemens Corporate Research in the United States, the consortium even incorporates centres of excellence beyond Europe and provides a unique association of competencies with internationally highly renowned partners.

Another element that imposes a Europe-wide approach is that like most serious paediatric diseases, also those considered in this project are relative rare. JIA has a prevalence of 1 in 10,000, Duchenne 1 in 4,000 males, spinal muscle atrophy is 1 in 20,000. Only a European consortium can combine all the clinical expertise required to tackle these rare diseases.

B.3.1.1.8 Relation to other national or international research activities

MD-Paedigree will build upon a decade of developments in infrastructure development initially pioneered in the European FP5 MammoGrid and FP6 Health-e-Child projects, which were then further advanced

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in FP7 Sim-e-Child. In particular, MD-Paedigree leverages on the grid Gateway concept, allowing scientists to abstract from the complexity of underlying grids, clouds and other computing resources they need to use. Open source tools developed in the EU FP7 project Khresmoi and the German government project Theseus-Medico will also be leveraged for retrieval and query formulation by the partners being involved also in these projects. MD-Paedigree will also assure compliance of the infostructure with the two EU FP7 Open Access projects OpenAIRE and OpenAIREplus which deal with the implementation of policies of scientific results.

Besides advancing multi-scale models from the FP6 Health-e-Child and FP7 Sim-e-Child projects in the cardiology and rheumatology disease areas, MD-Paedigree will leverage from the FP7 project NMS Physiome on the Neuro-Musculo-Skeletal Physiome to further advance software applications for the processing of imaging and gait analysis data into a full and robust musculoskeletal model.

Conceived of also as a specific implementation of the VPH-Share project, and thus fully interoperable with it, MD-Paedigree has the ambition to be the dominant tool within its purview. While not planning to directly relate to p-Medicine (due to closer vicinity of scope with VPH-Share), the interoperability table between these two infrastructural projects ensures that MD-Paedigree interoperates to some extent also with p-Medicine.

B.3.1.2 Applications scenarios

MD-Paedigree represents a major step towards personalised paediatric e-Health, based on data-driven models, patient-specific simulations and a sustainable data and model repository. MD-Paedigree can in fact impact the way healthcare is practiced in the future. MD-Paedigree’s impact on dealing with very concrete and exemplary clinical cases is demonstrated through the following applications scenarios:

B.3.1.2.1 Cardiomyopathies

Jonathan is a 12 years old boy with Duchenne Muscular Dystrophy (DMD). At clinical evaluation the child reported no dyspnea at rest with some fatigue at mild exercise. Jonathan was free of cardiovascular treatment, heart rate was mildly increased and blood pressure was low-normal. Echocardiography provided information on cardiac geometry and chamber function. A dilated left ventricle with left ventricular hypertrophy was seen. Systolic function was low-normal and diastolic function analysis demonstrated increased left ventricular filling pressure. Additional evaluation with CMR demonstrated a frankly dilated ventricle with mildly reduced ejection fraction and mild diffuse fibrosis of the cardiac muscle. The mitral annular plane was dilated and mitral insufficiency was caused by leaflet tethering. The patient was treated according to current clinical guidelines and a follow-up clinical evaluation and echocardiogram were programmed after three months to evaluate the effect of treatment.

To date, in clinical practice, information on cardiac pathophysiology is based on scattered information derived from different diagnostic techniques. In the present case, relevant information is derived from clinical examination, personal interview, echocardiography and CMR. Clinical outcome highly depends on the physician’s experience and ability to manage and exploit the different information sources in a time-consuming process.

MD-Paedigree provides a physician with a robust, multi-scale 4D anatomical, hemodynamic and electromechanical model, in order to integrate all available clinical and diagnostic data. Integrated information on cardiac geometry and volumes (obtained from CMR) is merged to functional information obtained from echocardiography (including filling pressure and cardiac synchrony) and hemodynamic data obtained from clinical examination. Beyond data integration, electromechanical and haemodynamic models of the heart give the possibility to understand the mechanism of muscle dysfunction by integrating information on muscle fibrosis and systolic mechanics and predict the impact of therapy in reducing mitral regurgitation, filling pressure and thus relieve symptoms. Treatment is personalised and tailored to robustly-modelled cardiac morphology and function, integrating all available information on heart geometry, ejection function, heart relaxation, ventricular inter-dependence, valve function and cardiac workload. Prediction of response to drugs helps a physician in prescribing the most effective

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treatment at the first evidence of cardiac disease. MD-Paedigree’s models reduce the timeframe from evidence of disease to optimal medical treatment, thus significantly improving patients’ morbidity and mortality.

B.3.1.2.2 CVD risk in obese children

Rose, a 17 years old obese adolescent, suffers from impaired glucose tolerance, high blood pressure, and irregular menstrual periods. Her waist girth is 102 cm. Rose has undergone the fasting measurement of markers of systemic inflammation, the US and MRI evaluation of adiposity (visceral, subcutaneous and epicardic fat estimates including thickness and volume), and the evaluation of cardiac morphology and haemodynamics by echocardiography. Arterial stiffness is estimated by means of the radial applanation tonometry, and a cardiopulmonary exercise test is also performed.

Her waist girth has significantly increased over the last 18 months demonstrating central fat distribution. Her fasting glucose is elevated and her oral glucose tolerance is markedly impaired. The patient is dylipidemic, with increased levels of inflammation markers. Echocardiography demonstrates left ventricular hypertrophy with normal systolic function and impaired cardiac relaxation. The additional evaluation with CMR shows a significant amount of pericardial fat paired with the mild diffuse fibrosis of the cardiac muscle. The cardiopulmonary exercise test on a treadmill highlights reduced tolerance to physical activity, with increased oxygen consumption and an evident pathological blood pressure profile. The applanation tonometry shows reduced arterial compliance, increased wall stress and impaired endothelial function.

MD-Paedigree provides the physician with integrated information on Rose’s cardiovascular structure and function, together with the quantitative assessment of fat distribution in the body, and metabolic and genetic data obtained from laboratory tests. The electromechanical model of the obese child’s heart allows understanding the mechanism of cardiac muscle and vascular dysfunction by integrating related information on systemic fibrosis, inflammation and cardiovascular mechanics; it also allows prognosis of disease development and predicting the impact of selected therapies and weight loss for the specific cardiovascular function, fat distribution and exercise tolerance. Treatment is personalised and tailored to the cardiovascular and metabolic phenotypes, personal habits and life style, and integrating all available related information on anthropometrics, demographic data, cardiac geometry and function, vascular compliance, genetic and metabolic profiles. Accurate estimation of cardiovascular risk, prognosis of disease development and prediction of the success of a selected therapy, based on the clinical history of previously observed cases in the digital repository, helps a physician in selecting the most effective treatment already at the first evidence of disease.

B.3.1.2.3 Juvenile Idiopathic Arthritis

Chiara and Simona are affected by JIA. Age at disease onset was 4 years old for both girls. Antinuclear antibodies were positive in both cases. Both of them had an asymmetric involvement of knee and ankle at disease onset. Within 1 year from disease onset both patients showed wrist involvement and started a treatment with second line agent (methotrexate). Chiara (Patient 1 in Figure 19 left side) experienced a severe and irreversible structural damage progression, as revealed by a 4-years follow-up plain radiography. Conversely, Simona (Patient 2 Figure 19 right side) developed a milder course of the

Figure 20

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disease and the 4-years follow-up plain radiography showed no significant signs of structural damage. Notably, at disease onset demographic, routine clinical and laboratory data did not significantly differ between these two patients. It was not possible, therefore, to distinguish, at disease onset, which of the patients would develop a more aggressive disease.

By integrating in a multi-scale integrated model clinical, MRI and US evaluations, immunological and meta-genetic data (microbiote), as well as the results of biomechanical analysis, we aim to identify outcome predictors and discriminate, early after disease onset, patients who will develop a more severe course of the disease and will require an earlier and more aggressive treatment.

A comprehensive model of JIA-related changes in the ankle will be available. Different imaging modalities (MRIUS) provide information to classify the degree of bone erosion and synovitis in both regions. For this, automated image analysis tools will be developed to reduce the time necessary for performing that task but also becoming independent from the individual observer who does the exam. Enhanced biomechanical models are generated by adapting highly sophisticated standard models to the individual case and thus predicting locomotive changes caused by JIA.

These are tested against results of a personalised gait analysis that further enriches this model by providing more details about the locomotive constraints for the considered joints. In addition, a selected sub-group is examined for a second time in order to record the data basis necessary for modelling also the progression of the disease.

The related database is built on the outcomes of the Health-e-Child project and contains information about the morphological changes visible in the image data but also about clinical, immunological and genetic and metagenetic (microbiote) data. Having access to a large repository of such classified individual JIA cases that are described in great detail – also longitudinal aspects – makes it possible to better predict the progression of the disease and to provide the best adapted medication for these patients.

B.3.1.2.4 Neurological and neuromuscular diseases

François is a 10 year old boy with cerebral palsy (CP). The medical records of the milestones of his motor development show a consistent significant delay with respect to his peers with normal growth. His current level of the so-called Gross Motor Function Classification (GMFCS) is 2, meaning that he can walk unsupported but has difficulties while walking outside. Actually his complaints are frequently falling on the playground, and very limited walking distance, due to early fatigue. Moreover, his physiotherapist, who has been specifically trained and has ample experience with children with CP, is concerned whether François’ walking pattern will deteriorate in the coming future, resulting eventually in wheelchair dependency.

François is referred to a specialist paediatric centre of rehabilitation medicine in Nantes, where his walking pattern is analysed in the gait laboratory. A complete recording of his gait pattern, using 3D kinematics (i.e. in fact 4D), joint kinetics and muscle activation patterns, is performed along with metabolic measurements of the energy cost of walking (ECW).

The physiatrist in charge of interpreting the results of the gait analysis of François concludes that hyperactivation of the calf muscles (m. gastrocnemius) is present, while at the same time this young boy is walking with slightly flexed knees during stance. This positioning causes compensatory hyperactivation of muscles at other levels, resulting in an overall increased ECW. The analysis is clear, but now the physiatrist should decide on the therapy. Unfortunately he can only rely on scattered information derived from some single cases, he learned to know at some courses, rather than explicit design rules. Current knowledge tells him that chemo denervation of the calf muscles (using local Botox injections) should normalize its hyperactivation, end hence compensatory activation and decrease the enhanced ECW. However, it would also contribute to a higher knee flexion moment, that would drive the knee in further flexion during stance, resulting in a further worsening of his walking, an outlook the therapist is afraid of. This latter phenomenon alone would call for another therapy, i.e. stiff carbon ankle-foot orthoses, that is known to be effective to counteract knee flexion in stance. So now the physiatrist is faced with the dilemma of what to do.

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It is exactly this kind of clinical decision making problem that profits from being informed by multiscale reusable models to be built upon the results of the Health-e-Child and the Sim-e-Child projects. This means (a) the development and use of a paediatric musculoskeletal model applied to gait, and (b) building a repository of many clinical cases, based on standardised gait analysis protocols, that generates decision rules derived by probabilistic modelling.

Fortunately, the gait laboratory of Nantes has committed itself to apply the EU standards of clinical gait analysis and became a registered user of the Model-Guided European Paediatric Digital Repository since 2016. all information regarding the case of François is uploaded into the system. In return, the disease modelling analysis shows that chemo denervation of the calf muscles is likely to solve the problem. However, the effect on knee flexion remains undecided, within acceptable model accuracy, for this particular case. Running the probabilistic model supports the treatment choice, and points towards two matched cases from KU Leuven. In those cases, chemo denervation of the calf muscles with additional intensive physiotherapy to prevent enhanced knee flexion proved to be successful. The physiatrist is now confident that François will profit from the treatment as indicated on the short and long term.

B.3.1.3 Societal impact of applying VPH to paediatrics: the crucial role of outcomes analysis

Even though life expectancy has very significantly increased in the last 150 years, the financial consequences associated with people living longer than expected – longevity risk – have received less attention than the economic and fiscal effects of an ageing society. Furthermore, the crucial difference between life expectancy and healthy life expectancy sharply conditions the economic outlook of healthcare systems, highlighting the importance of longitudinal studies assessing what can be the consequences of appropriate preventive medicine in young age. “Supporting healthy ageing means both promoting health throughout the lifespan, aiming to prevent health problems and disabilities from an early age”.

It can therefore be stated that in the longer term the VPH scientific approach requires a fundamental research investment in paediatrics, where the conceptual revolution which is underway, transforming the nature of healthcare from reactive to preventive, can best be applied, moving towards an approach based on personalised, predictive, preventive, and participatory (P4) medicine, increasingly focused on wellness.

Hood and Friend have stated that “there will be two major challenges to achieving P4 medicine—technical and societal barriers—and the societal barriers will prove the most challenging. How do we bring patients, physicians and members of the health-care community into alignment with the enormous opportunities of P4 medicine?”

We have already seen some of the reasons why MD-Paedigree is best positioned to respond to these challenges. It’s now possible to highlight why MD-Paedigree can do so while exerting also a significant societal impact.

Spending on healthcare in Europe and in the US has grown more rapidly than the economy. Difficult as it may be to assess directly the impact of technological change on health care spending, “P4 medicine promises to reverse the ever escalating costs of healthcare – introducing diagnosis to stratify patients and disease, less expensive approaches to drug discovery, preventive medicine and wellness, and exponentially cost decreasing measurement technologies”. The European Commission has rightly assumed that “new technologies have the

Figure 21 - Life curves for the UK, by year of birth Proportion of persons born in a given year surviving to successive ages (Source: Office of National Statistics, quoted in: IMF, Global Financial Stability Report, April 2012)

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potential to revolutionise healthcare and health systems and to contribute to their future sustainability”, but it must be noted that this assumption contrasts sharply with the generalised notion that healthcare expenditure necessarily increases faster than incomes and that new technology is a cost driver, as for instance in an important official US document, such as the one issued by the Congressional Budget Office in 2008, stating that “technological advances are likely to yield new, desirable medical services in the future, fuelling further spending growth and imposing difficult choices between spending on health care and spending on other priorities. If the health care system adopts new services rapidly and applies them broadly in the future – as it has tended to do in the past – then large increases in health care spending are likely to continue.”

In fact, the tangible outcomes of healthcare IT over the last 30-years are a witness that not every technological breakthrough has been proven cost-effective and that, even those that have proven so, have not necessarily been adopted due to technological (e.g. integration in other legacy systems) or non-technological factors (including ethical, legal, sociological and educational) factors, which were not addressed right out at the onset of the technological development.

MD-Paedigree addresses these issues by focusing on paediatrics, and by focusing on outcomes analysis as a key both for validating model-based personalised predictions and for new personalised clinical workflows.

The necessary conjunction of sustainable healthcare expenditure and universal world-class provision will only be ensured if the extension of personalised care to paediatrics, including preventative care, can become the trigger for the transformation of the entire health system through model-guided medicine as an overarching aim of the EU. “The sustainability of healthcare systems is becoming the number one issue in a number of member states …, [where] some common requirements are emerging, [i.e.] to maximise the yield of biomedical research expenditure; to achieve personalised healthcare for individuals and groups (women, children, etc.); to improve the reliability, repeatability, and the timeliness of medical decisions; to integrate digital health information on a global scale”.

Model-driven clinical workflows and evaluated outcomes-oriented management starting from paediatrics can replace the black-box (hidden, pragmatic) approaches to healthcare systems (including diagnostic, therapeutic, systemic, and managerial) and exert fully their role of change drivers. Only such a disruptive reform can achieve the levels of transparency which are now necessary for it to be politically feasible to continue also in the longer term to deliver to EU citizens healthcare systems based on the principle of socio-economic solidarity.

As such, and as already stated at the very beginning of our former IP, Health-e-Child, the ultimate goal of MD-Paedigree is that of becoming “a universal biomedical knowledge repository and communication conduit for the future, a common vehicle by which all clinicians will access, analyse, evaluate, enhance and exchange biomedical data of all forms.”

B.3.2 Plan for the use and dissemination of foreground

B.3.2.1 Dissemination & Training

MD-Paedigree’s outcomes are going to exert an impact on a variety of different work and research communities, ranging from clinicians and caregivers to biomedical researchers, as well as European industry, and finally also public opinion at large, where issues related to children’s health always appear to be particularly media attractive. This makes of MD-Paedigree’s dissemination a key and complex activity, deserving constant commitment during the lifetime of the project.

This task’s leadership within MD-Paedigree is assigned to P22 Lynkeus, already formerly in charge of the same responsibility in the preceding FP6 Health-e-Child IP and in the FP7 Sim-e-Child STREP.

The overall criteria is to ensure that the integrated work of the project dedicates a primary focus on dissemination activities, guaranteeing a balanced attention to all partners, according to the relevance of

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their respective results, stimulating within the consortium constant internal emulation for excellence.

Another general criterion for MD-Paedigree is to preserve the successful practice, developed both in Health-e-child and in Sim-e-Child, to disseminate the work produced in layperson terms rather than in an academic or industrial language.

Furthermore, within the dissemination effort, MD-Paedigree is going to attentively take care to avoid raising unrealistic expectations with regard to the speed of uptake of the new technology applications for fear of diminishing the credibility of its VPH results. The real proof of the cake is going to be the incorporation of the technological outcomes into new model-driven workflows for the routine activity of the participating clinical centres.

As explained in the dedicated WP18, general dissemination activities shall involve regularly reviewing overall progress of the project and provide an accurate overview of the current state of the art, highlighting existing goals and defining future ones.

These processes will be based around maintaining an appropriate project communication infrastructure, including a project website and document repository, possibly developing a deep interconnection with relevant parallel e-infrastructures such as GÉANT, VPH-Share, p-Medicine, SemanticHealthNet, DebugIT, OpenAIRE, OpenAIREplus, epSOS, HealthGrid, VPH NoE, Biomedtown, BioMedBridges, eHealth, and other.

Following this key-setting, MD-Paedigree dissemination’s activities will take the form of “knowledge sharing”, planned to be bidirectional; so that MD-Paedigree disseminates its results, while attempting to bring in external organisations and experts to share their expertise and experience, making possible to involve further clinical centres in the initiative, expanding the base of data and knowledge in the consortium. Indeed, the MD-Paedigree’s Consortium is fully aware that the reliability of its models will be directly related to the quality, depth, and volume of clinical data accruing into its federated digital repository. For this reason, MD-Paedigree aims also to develop closer cooperation with leading international organisations and projects (leading to a potential increased market size for EU industry), also seeking to find avenues for future expansion.

To facilitate this process, ensuring a more effective connection with the scientific community, MD-Paedigree shall take care that information about new resources (such as data or software) developed within the project is brought to the attention of biomedical and technology VPH researchers in a highly accessible way. Additionally, MD-Paedigree is going to communicate its work fully with other VPH projects to avoid duplication of efforts and to ensure greater compatibility amongst the outcomes of the projects.

A key part of the consortium’s dissemination work will take place through the various VPH Community meetings, and MD-Paedigree’s annual internal reviews on each of the three Actions in which the project is subdivided will also be the occasion for some selective high-level dissemination organised in conjunction with the Scientific Committee.

In an approach which has proved to be fruitful for Health-e-Child and Sim-e-Child, MD-Paedigree’s conference participation will seek to be done in conjunction with similar projects (e.g. shared booths). By benefiting from the reduced costs the project with be able to increase the frequency of conference attendance with booths designed to demonstrate MD-Paedigree’s disease modelling developments and the expanding digital repository functions, as well as with the project’s representatives. For these events specific posters and dissemination materials will be produced. Apart from booth-based conference attendance MD-Paedigree will also seek to participate in demonstration competitions and organise seminars and workshops throughout Europe and the rest of the world, taking into account the following indicative list of recurring annual conferences: World of Health IT (WoHIT), Bio-IT World Conference, Innovations and Investments in Healthcare (IIHC), Government Health IT Conference (GHIT), Healthcare Information and Management Systems Society (HIMSS), Connecting Healthcare IT (conhIT) , EC’s eHealth Week, HealthGrid, VPH NoE, Medical Image Computing and Computer Assisted Intervention (MICCAI), International Society for Magnetic Resonance in Medicine (ISMRM), ICT-BIO Conferences, Italian National Conference on Health Research, WHO’s eHealth Initiative, Health Informatics, Association of European Paediatric Cardiology (AEPC), European Federation of

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Neurological Associations (EFNA), European Alliance of Neuromuscular Disorders Associations (EAMDA), European Academy of Childhood Disability (EACD), Association for European Paediatric and Congenital Cardiology (AEPC), European Society of Movement Analysis for Adults and Children (ESMAC ), SMA Europe, TREAT-NMD, Orphannet, Federation of European Microbiological Societies (FEMS).

A detailed list of the events which MD-Paedigree’s partners are likely to attend to within 2015 is the following:

Event When Where VPH Workshop on Clinical Data Management and Sustainability

17th-18th March, 2015 Amsterdam, The Netherlands

2015 Cardiac Physiome Workshop 8th-10th April, 2015 Auckland, New Zealand

conhIT 2015: Connecting Healthcare 14th-16th April, 2015 Berlin, Germany

Bio-IT World Conference 21st-23rd April, 2015 Boston, MA (USA)

Insigneo Showcase 8th May 2015 University of Sheffield, UK

AEPC 2015: Association of European Paediatric and Congenital Cardiology

20th-23rd May, 2015 Prague, Czech Republic

MIE 2015: Medical Informatics Europe 27th-29th May, 2015 Madrd, Spain

EACD 2015: European Academy of Childhood Disability

27th-30th May, 2015 Copenhagen, Denmark

ISMRM 2015: International Society for Magnetic Resonance in Medicine

30th May- 5th June, 2015 Toronto, Ontario, Canada

FEMS 2015: 6th Congress of European Microbiologists

7th-11th June, 2015 Maastricht, The Netherlands

FIMH 2015: 8th International Conference on Functional Imaging and Modeling of the Heart

25th-27th June, 2015 Maastricht, The Netherlands

ECAI 2015: International Conference 7th Edition

25th-27th June, 2015 Bucharest, Romania

IEEE Big Data Congress 2015 27th June – 2nd July, 2015 New York (USA)

ICIMTH 2015: International Conference on Informatics, Management and Technology in Healthcare

9th-12th July, 2015 Athens, Greece

MEDINFO 2015: eHealth-enabled Health 19th to 23rd August, 2015 Saõ Paulo, Brazil

Personalised Medicine 2015: 3rd International Conference on Predictive, Preventive and Personalized Medicine & Molecular Diagnostics

1st-3rd September, 2015 Valencia, Spain

ESMAC 2015: European Society of Movement Analysis for Adults and Children

07th-12th September, 2015

Heidelberg, Germany

EG VCBM: Visual Computing for Biology and Medicine


University of Chester, UK

IIHC 2015: 4th Innovations and Investments in Healthcare Meeting


Munich, Germany

PICS ~ AICS 2015: Pediatric And Adult Interventional Cardiac Symposium

18th-21st September, 2015

Aria, Las Vegas (USA)

EHFG 2015: 18th European Health Forum Gastein

30th September - 2nd October, 2015

Gastein, Austria

MICCAI 2015: 18th Medical Image Computing and Computer Assisted Intervention

5th-9th October, 2015 Munich, Germany

EDF2015: European Data Forum 6th-17th November, 2015 Luxembourg

Table 26: Most important dissemination events for the MD-Paedigree Consortium in 2015

MD-Paedigree’s partners will also take part to various concertation activities, in order to meet the other

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FP7 funded project within the VPH topic, with the purpose to share results and achievements and also to possibly identify future activities and topics of common interest within the VPH community.

Furthermore, throughout the duration of the project all novel clinical and technologic developments will be submitted for publication in international scientific journals and disseminated through a biannual MD-Paedigree Newsletter, aiming to ensure that biomedical researchers and clinicians, as well as academia and industry, will be regularly informed about the project’s developments and ready to exploit the potentiality of its outcomes.

Building on the belief that a main goal is to link MD-Paedigree outcomes with clinical practice, the dissemination activities will be developed in close connection with the training.

The training activity will be carried out by UCL, that have gained a meaningful experience within the DISCIPULUS (Roadmap Towards the Digital Patient) project. Building on that experience, that brought out the importance of training as the most solid and long-lasting dissemination strategy, MD-Paedigree will organise a number of dedicated workshops with the key aims to expose the outcomes achieved both in disease modelling and in building the infostructure, highlighting the potential for change management and innovation in the participating clinical centres.

Presented by the different WP and Tasks leaders, specially convened “scenario analyses“ sessions, with the key personnel from both the clinical and the technological partners, will aim at pre-empting unforeseen technical uptake problems and establishing a smooth and ongoing dialogue between technology developers and end-users within MD-Paedigree.

The results of the previous workshops will be presented to the Scientific Committee and to the Users’ Board in order to assess their relevance and applicability, so as to refine the outcomes for a validation workshop and for a final MD-Paedigree Conference, to be held at the end of the project, targeting both internal and external clinical and research communities as well as patient organisations and the interested media.

B.3.2.2 Exploitation

MD-Paedigree builds on the considerable interaction that has been taking place between Health-e-Child, Sim-e-Child and EGEE and the success that was achieved by the two preceding projects within the EU Health Grids, e-Infrastructure and VPH communities: Health-e-Child won the Best Live Demonstration prize at the EGEE Users Forum in 2008, the ICT08 “Best Exhibition” award, and was showcased at the Final Review of the EGEE II Project. It was selected by the European Commission to be invited to present its work at the ministerial day, hosted by the Spanish EU Presidency and the Regional Government of Catalonia which preceded the joint World of Health IT and EU’s eHealth week in March 2010. Sim-e-Child, in its turn, was showcased by GÉANT at ICT 2010 in Brussels as an example of how the data networking services developed within Bandwidth-on-Demand and performance monitoring tools were having an impact on EC funded research; it has also had demonstration booths at ConHIT in Berlin in April 2011, at the WHO’s eHealth pavilion during the ITU Telecom World 2011 symposium (Geneva, October 2011), and at the American College of Cardiology annual conference in Chicago in March 2012.

Currently developed and exploited in its first phase as the Paediatric Cardiology Digital Repository (PCDR) by OPBG’s cardiology department, MD-Paedigree is therefore building on a solid base and is already attracting significant attention by industry as a timely initiative for standing up to the Big Data Market challenge.

Its attractiveness stems from being a federated database with a single platform providing all the essential services, as well as the computational infrastructure, for accessing clinical images, integrated patient data records from all available sources, disease models, similarity search functionalities, and therapy outcomes. MD-Paedigree will thus not only be facilitating the construction and operation of new decision support tools for prediction and simulation in paediatrics, as well as of the relevant VPH workflow models, open to further collaboration with a growing number of paediatric clinical centres, but will also open the way for having functional databases to assist drug developers in fostering model based-drug

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development (MBDD).

In order to assess the exploitation impact, MD-Paedigree will perform appropriate health technology assessment, with the key contribution of P21 EMPIRICA, who have already gained a considerable experience, also within two FP7 funded projects (VPHOP and MS-Physiome projects), in the field of assessment of clinical and socio‐economic impact of complex technologies such as multiscale computer modelling of human physiology. Within its first year and a half, MD-Paedigree will organise a Strategic Exploitation Seminar involving all its partners and leading to drafting a First Exploitation Plan by the end of year 2.

The MD-Paedigree consortium involves also a world-leading industrial partner such as Siemens AG, in the belief that this can greatly enhance the future exploitation of the project’s outcomes. All MD-Paedigree partners share the idea that ultimately exploitation is part of the value system instantiated by the linkages between all the visionary clinicians, biomedical researchers, industry and entrepreneurs who are willing to bet on its success, and shall be keen to promote spin-offs or and/or an ad hoc company for fostering MD-Paedigree future market outreach.

B.3.2.3 Management of Intellectual Property

The MD-Paedigree Consortium Agreement will address specific IPR concerns, as outlined in Part C of ANNEX 2 of the EC’s “Model FP7 Grant Agreement” (http://cordis.europa.eu/fp7/calls-grantagreement_en.html), on the basis of the following three principles:

• Ownership of background is not affected by participation in the project.

• Where MD-Paedigree partners will have jointly carried out work generating foreground and where their respective share of the work cannot be ascertained, recognition shall be given that some corresponding part of such foreground is to be considered as joint ownership. Each partner will participate to such joint ownership in proportion to the amount of efforts performed in the project.

• Dissemination activities shall be compatible with the protection of IPR, confidentiality obligations and the legitimate interests of the owners of both the background and the foreground. All IP related topics will be discussed and decided in the Governing Board as needed.

B.4 Ethical issues

Ethical aspects in MD-Paedigree project will be handled by an Ethical and Legal Committee that will be established to ensure that all the project’s activities are in keeping with the relevant national, European and US regulations.

In particular, the Ethical and Legal Committee will have the following functions:

Monitor the process of seeking local Ethical Committees clearance;

Examine the yearly Work Plan for ethical or legal questions and approve release;

Monitor and review project deliverables authorizing release where ethical questions arise;

Monitor for upcoming ethical and legal implications;

Propose solutions to legal and ethical questions coming from the work package leaders.

The Committee will be chaired by prof.ssa Prof. Laura Palazzani, Professor of philosophy of law and biolaw at the Free University Maria SS. Assunta (LUMSA) in Rome, Vice-president of the Italian Committee for Bioethics, Member of the European Group of Ethics in Science and New Technologies (European Commission). The Committee will be composed by representatives of the clinical partners, a member of the Scientific Committee, a representative of the Management and Technical Coordination Board, and by internationally recognized experts in ethics, bioethics and legal aspects. A component will be Prof. Maria Luisa Di Pietro, medical doctor specialized in legal medicine and in endocrinology,

http://cordis.europa.eu/fp7/calls-grantagreement_en.html

http://cordis.europa.eu/fp7/calls-grantagreement_en.html

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Professor in Bioethics at the Università Cattolica del Sacro Cuore and at the Pontifical Institute John Paul II - Lateran University in Rome, member of the Italian National Bioethics Committee. Further members will be appointed upon the project’s commencement, should the present proposal be approved.

The Ethical Committee will support the decision making process in legal and ethical issues. It will work in parallel with MD-Paedigree’s Scientific Committee to ensure ethical clearance to the results developed within the project’s lifetime and to the procedures adopted by each partner, and will have advising power toward the consortium.

The legal framework to handle ethical issues differs from country to country and on the nature and technical content of the information itself. Additionally the work to be carried out in genetic and genomic fields raise ethical issues that may be currently unforeseen. For these reasons, the creation of an ad hoc Ethical and Legal Committee, with the essential contribution of internationally recognized experts, seems to be necessary to the optimal development of MD-Paedigree’s activities and outcomes.

The nature of the work to be done in the project entails the handling of data tied to the physical person or to well-defined groups of paediatric population. The tasks in WP3, WP4, WP5, WP6 and WP 7 (Action 1) will address a range of bioethical matters connected with clinical research in paediatrics. These include informed consent, diverse ethical approaches in different European countries, use of existing clinical data.

The data necessary to conduct the project are:

a) new data deriving from the patients enrolled in the project (around 180 – 200 patients for each clinical research area: cardiomyopathies, cardiovascular disease risk in obese children, juvenile idiopathic arthritis, cerebral palsy, Duchenne muscular dystrophy, spinal muscular atrophy 3);

b) existing anonymised data previously acquired for research purposes and for daily health-care routine.

The nature of such data is further described in the 2 following paragraphs:

B.4.1 Patient enrollment and use of new data

Dedicated clinical protocols and informed consent forms for the different areas of clinical studies will be prepared according to current National, EU and USA laws and regulations, and seek approval from each clinical partner’s local Ethical Committee, prior to the commencement of the patient enrollment and data collection and handing phase.

As Coordinator, OPBG will centralize this process and act in conjunction with the MD-Paedigree’s Ethical and Legal Committee. At the end of the process new data from approximately 640 patients will be involved in the project.

In this context MD-Paedigree will adhere to operational principles that are consistent with the Convention on Human Rights and Biomedicine (Oviedo, 4.IV.1997), its Additional Protocol concerning Biomedical Research (Strasbourg 25.I.2005) and the Helsinki declaration in its 2008 amendment. This ensures that appropriate consent has been obtained for the envisaged use of data, prior to anonymization. Obtaining appropriate consent however is the responsibility of the original data generators. The transfer of data will only involve anonymised data in agreement with the definition given in the Council of Europe recommendation (rec 2006/4 of the Committee of Minister of the Member States). This use is in agreement with EU directive 95/46/EC on the protection of individuals with regard to the processing of personal data and free movement of such data. All these activities will be coordinated by partner 1 - OPBG which has a longstanding experience in merging data from international healthcare databases.

All measures will be taken to ensure the confidentiality of families and of the children participating in these studies. In particular, Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of personal data will be complied with for data storage and handling in

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order to ensure patient data protection and confidentiality. Full comprehensive information will be given, according to the Directive of the Council of Europe Bioethics Convention, to all families and patients, if of appropriate age, participating in the MD-Paedigree’s studies. Written and oral information will be given to parents/carers, before written full informed consent is obtained for study participation. Information will be presented clearly, using short sentences and either avoiding or explaining all technical terms. Information sheets will be provided in the patients’ own languages. If neonates/infants are institutionalised, a fully informed consent will be obtained from the legal guardian in accordance with national and regional laws and regulations.

The following issues will be stated and explained in the Informed Consent Form:

• explanation of the research • purpose + duration + description of the study • foreseen risks (if any) • benefits • alternatives • confidentiality • treatment/compensation +information • contact for rights/claims • voluntary participation • possibility to withdraw consent • no penalty or loss on stopping

Regular updated newsletters will keep families informed about the progress of the studies.

Copy of each local Ethical Committees’ approval and of the informed consent forms and information sheet will be provided to the Commission with the first periodic report.

The Ethical and Legal Committee in addition will also oversee ethical aspects of the conduct of project activities and, together with the Scientific Committee, will make sure that the Oviedo Convention and the Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data (Strasbourg, 28.I.1981) are complied with.

Tools and services developed in MD-Paedigree aim at being used in clinical routine to support the decision making process, from diagnosis to treatment. Therefore any findings that may contribute to improve clinical pathways and decision making processes for any specific patient, will be appropriately communicated to the medical community. In order to increase the research capacity in paediatric medicine, the proposal aims also at developing harmonised and integrated research tools that can be used globally. If models and simulations are discovered that potentially improve decision making in choosing the best possible treatment for patients, these findings may need to be confirmed by appropriate clinical trials.

B.4.2 Use of existing data

In MD-Paedigree also existing data will be used. The existing data is anonimysed data previously acquired within the FP6 IP Health-e-Child and the FP7 STREP Sim-e-Child and other European projects, as well as data coming from the clinical partners’ daily healthcare routine.

The anonymised data deriving for the described EU projects, has been originally collected in accordance with national and European law and regulations, and with the prior consent of OPBG’s and each participants’ internal Ethical Committees. A new approval, specific for the project will be sought from the local Ethical Committee of each clinical partner providing such existing data.

The data deriving from routine clinical activity will be collected in accordance with national and European law and regulations, and with the prior consent of each clinical partner’s internal Ethical Committee.

All the procedures listed below will be used to protect the confidentiality of data and samples collected and stored for the project’s purposes:

Only authorized persons will be granted access

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Only authorized persons may enter and view study data

Passwords and system IDs will not be shared

Physical security of the workstations/files will be maintained

Adequate back-up plan is in effect

Staff trained on data entry system and importance of security procedures

Workstations with databases will not be left unattended

The cornerstone of ensuring appropriate ethical and legal conduct is located in the rules and regulations that govern each individual database that contribute patient records. These have a history of conducting studies and writing scientific papers. As a result, each database has experience in using data for research purposes. As databases are located in different countries, each database has to deal with a specific set of rules and regulations. Although framed in European law, exact implementation of European law may vary. As a result, each database has its own mechanisms for dealing with both European, national and local rules and regulations. Nonetheless, the Project’s Ethical and Legal Committee will ensure that all data used in the project has been properly anonymised and that local Ethical Committees have approved the sharing of the data.

Ethical question MD – Paedigree approach

What kind of human participants/data are involved within the research?

Patients - Children

Are all sensitive data that are planned to be collected really focused on the research question and is relevant for the foreseeable research?

As previously mentioned, during the lifetime of the project 2 kinds of data will be used: previously collected data, and data coming from patient enrolled for project purpose (see section 1 of Ethical issue). All the data will be collected according to local and European legislation and with prior approval from each clinical partners’ Ethical Committee.

All such data are relevant and necessary for reaching a number of data able to guarantee the projects aims: enhancing the existing disease models and establishing a worldwide advanced paediatric digital repository.

For how long will the collected data be used?

The data will be collected and exploited during the lifetime of the project. The data acquired during the project, all anonym, will constitute the basis not only for the building and extending the disease models, but also for the functioning of the system. To guarantee the deployment of the project’s result it is therefore essential that the acquired data remain in the systems and be used for its functioning.

For how long will the collected data be stored and when will it be irreversibly destroyed?

As mentioned before data will be collected within the lifetime of the project. At the end of MD-Paedigree’s activity all the anonymised data stored in the project’s database will remain in the system and will not be destroyed.

Do the applicants have the necessary legal permission to obtain and process the data?

As previously outlined data gathered from a previously set (by OPBG and from other projects)are covered by the initial informed consent for this complementary use of the data (see section 1)

How will the collected personal data be securely accessed?

Personal data will be duplicated but anonymised into the system. The exporter will run into each hospital secured network using standard technologies and will deliver only anonymous data to the external less secured system.

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How will the data be securely stored: data structure and format?

The data will be stored in a standard SQL DB on a secured server. Firewalls and security keys using certificates will be in place to secure data transfer and authentication. All data transfer will be encrypted. In any case accessible data will not permit to identify a patient if you are not connected in the hospital secured network.

How will the data be securely stored: location & hardware?

Data will be physically stored at the node location (in hospitals) on hard drives on computers connected by wire. Hard drives will be configured using RAID 5 Technologies.

How will data transfer be monitored?

Data transfer will only be authorised to other registered gateways or to authenticated users. Data won’t be shared to anybody or any place not fully registered in the system. A specific Virtual Organisation (VO) will be in place using all grid security protocols. Data sharing with open-source repositories will be taken into consideration in work package 14, T14.3 and T14.4

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