Health-033-19

TECHNOLOGY

INDUSTRY EXCHANGE NETWORK

IXN – CHARTAUCL Center for Global Health Economics

Member Name: Francesco Stefani, Matthis Peltier, Charles SmithClient: Dr Hassan Haghparast-BidgoliSupervisor: Dr. Yun Fu

User InterfaceIntuitive design and functionalities make it easy to use the app on a day-to-day basis

Question SpecificThe questions asked are rigorous and in line with the main academic standards

Multi PlatformThanks to the centralized technology, the app runs easily of different browsers and mobile operating system

Excel FriendlyThe data can be easily manipulated and download in an Excel to allow user flexibility in the analysis.

Statistical AnalysisThe paper's data are saved and quickly analysed by our algorithm in order to provide meaningful statistics figures

Multi AccessibilityDifferent users can use the app with tailored pages and actions

FEATURES

Ruby on

RailsPostgres

Azure

virtual machine

API

WWW

CHARTA

ABSTRACT

Charta is a web and mobile application that aims to facilitate the

appraisalof health researchpapers.

The app allows users to invite students and scholars to provide

feedback on academic papers and gather their responses

immediately.

This process prevents the hassle of having to send forms to

each reviewer individually before then computing their responses

within an excel file once the forms are returned. The main user

receives detailed statistical and graphical reports based on the

data gathered from the reviewers.

FUTURE DEVELOPMENTS

Preliminary check on papers using sentiment analysis via Machine Learning

Notification center to inform the users when a paper is uploaded

Integrate the app with Moodle login

simple UI which is easy to operate

for the GOSH DRE staffs. In the

backend, there is a program written

with python, which cleans, merges

and encodes the files so that the

data is ML (machine learning) ready.

We also produced a working GANS

(Generative Adversarial Networks)

using deep learning and neural

networks, which has the potential

to take in FHIR and none-FHIR data

and learn from them, then produce

synthetic data.

The goal of our project is to justify

that unstructured EHR (Electronic

Health Record) data can be

manipulated in such a way that it

can be learnable by a generative

machine learning model.

Synthetic Healthcare Data Generator with GANS

University College London | Team 1 | Zia Ali, Dylan Vekaria, Sifang Du Client | GOSH DRIVE

Overview & Solution

What is the issue?

With the introduction of GDPR, individuals

have more control over their personal

data and companies use the collected

data more cautiously under regulations.

However, the regulation also limits the

use of personal data in areas such as

research and software development.

How can we solve this problem?

The GOSH DRE team proposed to

generate synthetic data from real data

using machine learning. However, the

GOSH DRE team needs our help with

connecting the three parts shown below.

What are the requirements?

We need to build a pipeline which cleans

the data and encodes it to a machine-

readable format so that the data can be

passed into the GANS later.

Our final delivery

In our final delivery, we produced a

?

The user

interfaceThe data cleaning

programThe Peach GANS

Processor

?

Key Features

Upload raw datasets

Download cleaned datasets

Cleans, data automatically

A GANS that generates

synthetic data

v

The Architecture of GANS

Visualizations from GANS

The User Interface

ali.zia.17@ucl.ac.uk | dylan.vekaria.17@ucl.ac.uk | sifang.du.17@ucl.ac.uk Health-007-19

SMART ON FHIR MODULES

UCLAbstract

The Fast Healthcare Interoperability Resources (FHIR) is a standard for exchanging electronic healthcare data and records

between medical institutions and applications. SMART is a platform that builds upon the FHIR specification and provides developers with a set of APIs to create applications on top of FHIR.

Our aim is to help FHIR application developers who may not be too

familiar with FHIR, to discover the capabilities of SMART APIs and build applications for this next generation of digital healthcare. Currently, we are developing a web application that is a collection of modular SMART functions. It is a library of runnable code snippets that can act as a helpful tool and reference where building SMART applications.

With our application, a developer has access to a variety of JavaScript code snippets, copy them, edit and play around with them, and use them to not only become more familiar with FHIR and the SMART JavaScript client, but to help build their own SMART applications. Furthermore, administrative users can also

create and add their own code snippets to the library.

COMP0016 Team 2: Ralf Yap, Qinyi Tang, Ziyang Dong | Client: GOSH DRIVE

Requirements

Modularized features/functions that can be implemented by a user in their SMART app

Code snippets Description of each features Demo feature Ability to copy/edit code snippets Ability to combine functions User accounts and authentication to save code snippets

Feature curation – allow admin user to add new modules or edit existing ones

Key Features

UCL Internal CodeHealth-008-19

Abstract Requirements

Our Working Product

Solution Technological Structure

Commissioned by GOSH, our project aims to create a prototype of a

system where a wearable device is attached to the discharged patient. In

addition to collecting health data from the wearable itself, it also collects

data about the environment using separate sensors. These combined data

allow for more meaningful analyses, such as when a patient has a seizure,

the doctor is able to look at the info of the environment at that time, and

possibly determine the cause of the seizure (e.g high light & humidity).

•An IoT healthcare monitoring package

•Wearable device (Android WearOS)

•An array of different sensors that can be added or removed from a room,

depending on particular needs.

•Wearable device attached to the patient’s wrist, like a smart watch.

•When the patient is in proximity of the sensors, data from both the

wearable device and sensors will be sent to a cloud based system in real

time, where it can be further analysed in greater detail.

Wearable Sensor

Collects Heart Rate

Collect Data From

Environment

Historical Graph

Analysis

Strong User

Authentication

Web Application

Control Center

Azure Services

Team 3

Jiahui| Xin Deik | Adamos

eVitals Healthcare

Monitoring Solution

Health-009-19

IXN – SOTA/Jibo

as a patient

User Interface

Client: Great Ormond Street Hospital

Team: Phu Sakulwongtana, Cao Khanh Nguyen, Klajdi Lamce

Phone

Jibo

Computer

System

API

Open

Health DB

DialogFlow

API

Machine

Learning

Key Features

• Virtual patient imitates real patient• Personal login details with progress tracking• Provides feedback on disease matched with symptoms• Experiment new chat-bot using State-Of-The-Art Machine

Learning Models1

• Easier Extensible with Micro-Service Like Architecture

Abstract

When training medical personnel, one of the most important goals is for them to have the ability to interact and

communicate with patients in order to understand their condition. However, an approach consisting exclusively

of training with real patients has some restrictions including the availability of both parties and ethical issues.

Here, we present an extensible platform for people in the medical profession to practice basic interaction with

patients, whereby we replace real patients with a chat-bot. The users will be able to interact with a bot to

complete a certain goal, in this case, to diagnose the disease which the bot might have. We employ a powerful

differential diagnostic tool, Isabel, to act as the guidance system for users to measure their own performance

empirically. Furthermore, we explore the design of the chat-bot and propose a mix between a state-of-the-art

neural-network architecture with a rule-based system, powered by DialogFlow.

1, 2Attention Is All You Need, https://arxiv.org/abs/1706.03762

3

ISABEL: a web-based differential diagnostic aid for paediatrics: results from an

initial performance evaluation

Advisor: Dean Mohamedally, Yun Fu,Graham Roberts/Gemma Molyneux, Neil Sebire

Softwares Architecture

Isabel

API

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Front-End: Used React.js for the design of the dashboard, as

it is a very simple library that simplifies the creation of single

page application, making it fast, responsive and user-friendly.

Back-End: Used Django as our back-end solution as we found

it has the perfect balance between having secure and easy

set up as well as freedom to add our custom functionality.

Sensor-Hub: Variety of sensors were used with a number of

Raspberry Pi’s to implement the network of sensors that

collect the data.

HEALTHCARE SENSOR FUSION HARDWARE

Team 05: Nikolay Bortsov | Alexandros Frangos | Ahmed Fawzy

Client Names: Neil Sebire | Sue Conner | Gemma Molyneaux

GOSH DRIVE

System ArchitectureAbstract

The problem we were trying to solve was the identification of

the normal state for a room using variety of sensors. We

have set up the sensors with raspberry pi that communicates

with the backend using an API. The data from the sensors is

then displayed on the dashboard where the medical staff can

control the raspberry pi sensor hubs and record medical

procedures. The data from the sensors in also used in variety

of algorithms that work out the optimal condition for the

room to be operated in. In case of a bad entry the dashboard

flags that the room’s condition maybe not be the best on the

dashboard. We hope that with this technology, issues from

external factors can be found and prevented earlier to

improve the success rate of medical procedures.

Evaluation

The system is comprised from 3 parts: Dashboard, Sensor Hubs

and Learning algorithms. We tested the system extensively,

covering a large number of extreme conditions, placing sensors in

different places and situations. The system works flawlessly

without bugs and with such technology, problems occurring from

external factors can be prevented earlier to improve the success

rate of medical surgeries.

Future work

The sensor hub system could be made more scalable by using

wireless sensors as well as use easier sensor integration. Moreover

algorithms used for the learning of data, could be further

developed and analysed to work out perfectly the optimal

condition for the room to be operated in.

Displaying sensor

data graphically

Start an event for a

specific patient

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Computer Vision for Medical Instrument Detection

Team 6: Benedict Chan, Shirin Harandi

Supervisors: Dr Neil Sebire, Gemma Molyneux

Abstract:With recent advances in Computer Visions, current technologies are able to

identify vast numbers of objects in a wide variety of scenarios. By utilising

such technologies, we attempted to train machines to detect and identify

different medical instruments used in clinical environments. We wanted to

create a system that monitors medical instruments being used in operating

theatres.

The system will be able to correctly identify and display the different

instruments that are present in the operation tray, thus allowing all staff to

gain a clearer overview of the operation at any given time. By gathering this

data, complete operation summaries and timelines can be produced. All of

this real world data can be analysed and shared to all medical personnel to

learn and improve their skills. We hope our system will open up avenues for

object detection in the medical field and allow the field to advance further.

Requirements:• Unique logins for each doctor allowing operations to be

grouped together

• Emergency login for quick access to application

• Live video feed with detected instruments being identified

and highlighted

• List of all instruments used in the operation and the live

status of these objects

• Check that all instruments are present after the operation

• Full summaries can be seen for all operations

With GOSH Drive

Key Features:

Simple User Interface

Data Analytics

Cloud Based API

Live video feed with status of objects used in the operation

Health-012-19

INDUSTRY EXCHANGE NETWORKIXN – Developing Accessibility Technology for the GOSH Sound and Sight Hospital

Great Ormond Street Hospital

Harry Thomas, Xingyu Liu, Sidak Pasricha

Neil Sebire, Gemma Molyneux

Our project is to produce a mobile method of indoor navigation, forpatients of the Great Ormond Street Sound and Sight Hospital. We

hope to improve safety and patient independence within the hospital

by allowing users to navigate without the aid of parents or staff. The

system will allow users with hearing and/or sight loss to select adestination using an Android phone application, and receive turn by

turn directions through the use of haptic feedback. In our specific

deployment, we will be using the NTT Data supplied Buru-Navi to

provide haptic feedback to the user. However, we will design thesystem to allow for alternative output devices to be used.

Abstract Key Features

An easy to use phone

application, with 'Easter Eggs',

will make the experience

enjoyable for children.

Patients with either hearing or

sight loss will be able to easily

select their destination by voice

or touch.

Administrators can see a

heatmap of each location,

showing where users commonly

visit.

Using haptic feedback provided

by a Buru-Navi, we will guide the

user turn by turn to their desired

destination.

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INDUSTRY EXCHANGE NETWORKOptimising 111NHS DigitalGun Woo Park, Mohammed Chouman, and Ziying Cheng (TEAM 10)Joseph Connor – NHS Digital (Client)

Automating call quality analysis with

data samples

POC for prioritising needs by queuing of

the calls

Identifying triage outcomes from the

process pipeline for automating care

advice

References

AbstractThis project developed a toolkit which can

help 111/999 (emergency telephone

number) call handlers to optimise their call

handling practices for the NHS 111 and

999 services.

This is achieved by solving three problems

by the provision of tools to analyse : call

content, queuing optimisation and an

automated care advice system. This was

done using various natural language

processing techniques from Azure

Cognitive Services, IBM Watson, Google

Cloud and number of machine learning

techniques.

The final outcome of the project is an

application that runs on Windows, Linux

and MacOS platform.

LDA/t-SNE

This method enables topic extraction

and clustering. The program visualises

clusters of topics which is easy to

interpret.

[1] Optimising 111, Park, G. et al, 2019

[2] Bayesian Updating with Discrete

Priors, Orloff, J. et al, 2018, MIT

[3] Latent Dirichlet Allocation, Blei, D. et

al, 2003, Journal of Machine Learning

* Further references are provided in [1].

Key

Requirements

111

Call Handler

I want to…

• Automate the call

quality analysis

• Queue the calls

• Give care advice

efficiently

Image © 2019 Wikimedia, Microsoft,

Apple and UCL Team 10

HTTPS

User

Classifier

LDA/t-SNE

Ranker

Trigger words detector

DESKTOP

System Architecture Diagram Health-014-19

INDUSTRY EXCHANGE NETWORK

IXN – Interpreting Black Box Algorithms for the NHS

Company: National Healthy Service

Author: Ayushmaan Seth and Zhong Yi

Client: Joseph Connor

Abstract

The aim of the project is to interpret and explain in lay terms the various black-box algorithms and artificially intelligent models used in healthcare, especially the NHS.

The tasks are to explain why a decision was taken, what could have happened if the inputs were something else, how close were the inputs from the decision being flipped and how can we audit the model to make it suitable for the real-world data.Our solution was designing a web-app integrated with TensorBoard, Google's What If tool for explaining machine learning models as well as combining research projects such as LIME to perform a comprehensive analysis. This will be helpful especially to the NHS since after the enforcement of GDPR it is necessary to explain the outputs of a model.

Key Features

Sign up/Log inUsers can have their personal account and data storage for free & safety.

Personal DatasetUsers are able to upload their special dataset as the training dataset.

VisualizationOur application can analyze the dataset and explain the prediction in simple way.

Decision AnalyzationUsers can see the changes of predictions with different initial inputs in real time.

Key Requirements Solution

Main features need to be accessible for all potential users.

The script should be able to adapt any input dataset from users

Visualization results must be clear to the general public as well as to the NHS staff

Our analyzation and visualization services are built on Web Application by Flask

Our scripts based on Python can adapt different kinds of dataset flexibly

The visualization results from Lime and Tensorboard are comprehensive for public

Log in/Sign up Page Lime Prediction Page User Customization Page Tensorboard Visualization Page

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v

TEAM 12 – COMP0016 Systems Engineering

NHS AI PlayGround – AI Testing Platform

The aim of the project is to provide a secure and comprehensive web platform controlled by NHS for their clinicians to post challenges which are solvable by AI. The developers can work on the challenges on the platform itself and thereafter submit their solutions. Solutions will be evaluated by the clinician and ranked according to its accuracy.

The platform is a Django-based web project which are then further split into smaller Django applications e.g. challenges, solutions, users etc which handles different areas of the project. The on-site coding environment is provided for using JupyterHub and Azure Kubernetes Service is used to manage the hub itself.

The platform has additional features such as a discussion page for developers to interact and a tutorial page. The final product will be deployed onto Azure which will then be transferred to NHS for their usage.

Team Members (Team 12):

Haixiang Sun, Wei Tan, Zixuan Wang

Example of a challenge page

The discussion page for developers to share ideas and questions

1. User authentication and personalisedprofile

2. Differing functionality and interface for the two different user types (clinician & developer)

3. Ability to create challenges and upload data for clinicians

4. Provision of on-site coding environment (Jupyter notebook) for developers

5. Discussion page for collaboration and interaction between developers

TECHNOLOGIES USED

DjangoThe web application itself was developed using the Django framework. The tools used for front-end development are HTML and CSS while the backend is largely done using Python.

The application is connected to a MySQL database.

JupyterHub & Azure Kubernetes ServiceAnother major feature of the application is the provision of an on-site coding environment in the form of a Jupyter notebook.

The notebooks are spawned and configured using JupyterHub which is in turn managed using Azure Kubernetes Service.

POTENTIAL FUTURE DEVELOPMENTS

Client (NHS):

Joseph Connor

Advisors:

Dr Graham Roberts, Dr Dean Mohamedally, Dr Yun Fu

KEY REQUIREMENTS

1. NHS clinicians can create challenges and

specify challenge requirements such as

preferred algorithm and prize money

2. Algorithms can be tested on the platform

i.e. algorithms can be run on-site

3. Solutions can be submitted to the platform

to allow for evaluation by clinicians against

testing sets

The Jupyter notebook provided spawned using JupyterHub

Image Copyrights:©iconfinder

1. Currently, the site relies on clinicians

evaluating the models with external tools and

marking them manually on the solution page.

Such tools could be embedded on the

platform to check the accuracy of the

algorithm once the developer has submitted

his/her model, with the value displayed right

after automatic evaluation.

2. Developers can create models with only

Jupyter Notebook on the platform. Other

methods such as RMarkdown scripts could

be added to the platform in the future as

alternatives.

3. The tutorial section could be expanded to

allow developers to create their own tutorial

posts. Also, an introductory guide providing a

walkthrough of the features the platform

could be created to offer a better user

experience.

ABSTRACT KEY FEATURES

Health-016-19

Pregnant women with a high concentration of protein in urine, hypertension, or diabetes are advised to

frequently visit the hospital for testing to monitor for development of any complications, which can cause

anxiety for patients and have significant cost implications. Our project aims to mitigate the financial

implications and time consumption of this process by extending the Hampton app to allow patients to record

and monitor blood glucose and urine dipstick results through a mobile app while displaying these values to

doctors through a web app for their evaluation. The app will use image processing to analyse urine dipstick

results through pictures and include a chatbot to allow users to discuss their results. It will also alert patients

and doctors to abnormal results and advise patients to consult their doctor. Overall our app is intended to

make this frequent process efficient, cost-effective, and stress-free for both patients and doctors alike.

IXN - INDUSTRY EXCHANGE NETWORK

Client: Trakka Medical - Dr. Asma Khalil

Team 13: Yifan Liu, Zhizhe Xu, and Yomna Ghannam

Abstract

This solution should be integrated with the existing Hampton

Medical solution in order to provide one holistic solution for patient and

doctor use. Future iterations of this project could include support for

the analysis of multiple types of urine dipsticks and extend the usage

of the chatbot to answer questions beyond the frequently asked

questions. The solution may also be expanded to support the

monitoring of other information, such as sleep, water, or

macronutrients, to track the overall health of the patient during their

pregnancy. The app could then be extended to provide feedback such

as reminding the patient to drink water. These additions would allow

the app to serve as a single source for monitoring all the information

that may be important to track during pregnancy.

Future Work

Mobile Application Web Application

Upload new blood glucose/urine

protein test results

Check previous test results and

view them with a line chart

Delete any results that are

accidentally uploaded

Automatically detect urine protein

test results with image

Be alerted when test results are

out of normal range

Chat bot which helps answer

frequently asked questions

View patient data uploaded from

mobile application

Add and modify patient information

Be alerted when patients’ test

results are out of normal range

Trakka Medical Blood Glucose Monitor App for Pregnancy

Key Features

The designed solution consists of a mobile application that is intended for patient use as well as a web

application that is designed to be used by clinicians. The mobile application, which is built using Xamarin,

uses image processing to detect the results of urine dipstick tests through a picture taken by the user.

Moreover, the mobile application displays the blood glucose and urine test history to the patient both

numerically and graphically, alerts them to consult their doctor when their test results are abnormal, and

includes a chatbot which patients can use to respond to some frequently asked questions. Microsoft Azure is

used to host the website designed for use by clinicians as well as the SQL database that contains the

patient data. The website, which is built using ASP.NET, allows clinicians to view the test data entered by

their patients both numerically and graphically as well. Through the website, clinicians can add and modify

their patient list and their information and be alerted when patients input abnormal test results. The two apps

in conjunction allow patients and clinicians to closely and efficiently monitor test results.

Solution

Technological Structure

Blood Glucose Result

Submission PagePatient Information Viewing

and Editing Page

Front-End Back-End Data Store

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Health-018-19

Abstract

INDUSTRY EXCHANGE NETWORKAN IOT-ENABLED CROSS PLATFORM APPLICATION FOR GLUCOSE AND BLOOD

PRESSURE MONITORING FOR PREGNANT WOMENTRAKKA MEDICAL, GOSH

YANKE ZHANG, SONIA SHAH, YUSI ZHOU

DR. MOHAMEDALLY, DR. FU, ASMA KHALIL

Pregnant women and doctors are continually trying to

battle pre-eclampsia development by the continuous

monitoring of blood pressure and glucose levels. However,

experience has shown that it is difficult for the patient to

take these readings in the hospital on a daily basis.

The development of this cross-platform application

provides a new care pathway that gives the patient

flexibility and comfort to measure these readings on their

own and be notified when readings present a potential risk.

The application collects measurement readings from the

respective measuring devices via Bluetooth and uploads

them to a cloud database for monitoring by doctors,

without any cables or manual input. Patients are reminded

to measure the blood pressure and glucose levels at certain

times of the day and are notified of the readings obtained

directly on the application. This has been achieved through

the Ionic frameworks and firebase cloud technologies.

•Automated data entry via Bluetooth

•Integrated communication between application and cloud

database

•Instant notification and reminders to take readings

•Android and IOS platform friendly

Key featuresKey requirements

• Able to automatically receive input data read

from respective measuring device via Bluetooth

• Display collected data on the mobile application

• Able to upload the data to hospital system

database for monitoring by clinician

• Alert/pop up notification to patient when data

readings out of range is collected and doctor

contact details given

• Automatic notification alert/pop up to doctor for

data reading out of range and

• Doctor’s contact details provided

A scalable back-end data engine which distributes data

processing among a cluster of machines

An intuitive, generalized API allowing researchers to efficiently

interact with HaMpton data to run machine learning models

3A front-end web app visualisation which allows authorized

personnel to query and upload medical datasets

2

1

PEACH Engine for HaMpton Pregnancy

Bridging the gap between medical and data science professionals

Our task…

Pre-eclampsia is a leading cause of stillbirths among pregnancies, whose

early detection is an ongoing challenge in the field of Maternal-Fetal

Medicine. In the past year, Prof. Asma Khalil’s “ HaMpton Medical” app

has collected blood pressure data of pregnancy patients with the hope of applying ML to develop predictive models which can lead to

breakthroughs in early detection of pre-eclampsia. This project acts

as a preliminary first step to this ultimate goal by developing a

scalable data engine to house HaMpton’s growing dataset for

medical and data professionals alike, complete with generalisedAPIs to query datasets and features such as visualisation and

data formatter for machine learning readiness.

Our Solut ion…� Back-End data engine built on a Kubernetes computer cluster

running Apache Spark for data processing

� Easy-to-use APIs which process Azure Cosmos database requests for authorized data scientists

� Front-end web application for interactive data visualisation for medical specialists build on Django and Chart.js.

Addit ionally:• Filtering system for selective visualisation and query

• Hampton data cleaner for machine learning readiness

TEAM 15: Wiryawan Mehanda | Max Bert field | Selena Li

In collaborat ion w ith:

Key Features

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Health-020-19

NHS Surgery Camera System for GOSH and St Georges

Team 20: Kyla Aguillo, Venet Kukran, Kailun Shen

Abstract

Often, it is the case that medical students are unable to find sufficientresources from which they can learn the intricacies of surgery, without

having to struggle to find a surgery which they are able to watch in person.In order to better facilitate the learning of trainee surgeons, we aredeveloping a web application which can then be used to store and easilyaccess surgical video. The footage captured, using the Microsoft Kinect, willalso be reconstructed to deliver immersive, 3D views of surgery, in order toproduce the best possible learning resource for students. Various featureswill also be implemented to make the video finding and watching process asstreamlined and intuitive as possible, to produce a platform which can help

develop these students into the best surgeons they can be.

View all accessible recorded videos with information

Create video timestamps using speech recognition during recording

Play both 2D and 3D recorded videos

Key Features

Main page

Record video page Watch video page

Database

Save video page

LocalFile

upload

download

St George’s Hospital

GOSH DRIVE

Record and store videos

Key Requirements

SHARE

STORE

RECORD

Use Kinect camera connected to the web app to record surgeries with timestamps

capabilities

Recorded videos will be stored in 3D along with timestamps and video descriptions

Access to videos can be given to students

Health-021-19

VirtualRehab Hands: Fine Motor Rehabilitation Games

Group 26: Weixi Zhang, Yun Fang, Carlton Ji

Client: Evolv Rehabilitation Technologies

Stroke is the leading cause of adult disabilities and affects

around 100,000 people a year in the UK alone. VirtualRehab

Hands is a suite of modules that addresses and enables the

practice of fine motor skills in the hand area. Our project

pertains to the Exergames module - utilizing the Leap

Motion Controller to produce games that have a focus in

making repetitive exercises more engaging for the user. We

implemented this in the form of an endless runner style

game, using Unity and C#. By creating a fun, interactive and

engaging exergame, we expect an increase in player

enjoyability, the length of time before being bored and most

importantly, the effectiveness of hand rehabilitation.

Abstract

Key Requirements

A fun game that can be played by stroke

patients

Using Leap Motion sensor to detect hand

movements

Supporting multiple hand gestures for

creative game controls

Allowing modifications of dynamic game

elements from VirtualRehab, e.g., target

locations, target numbers and time

allowed to hit the target

A striking aesthetic style

Key Features

Health-022-19

Obstacles to be overcome by specific hand movements, with adjustable difficulty

Changeable game map/theme

Scorer

Target number indicator

Visualiser of player’s hand

Character controlled by player’s hand

System Architecture

openEHR Clinical Knowledge

ExplorerSupervised by Yun Fu UCL Internal Code:

Abstract:

Clinical data is usually distributed in different repositories

called Clinical Data Repositories (CDRs). openEHR is an

open standard that allows clinicians to develop technical

models of clinical information requirements that can be

rapidly deployed to vendor-neutral datastores to underpin the

data storage and querying requirement of a new breed of

clinical/patient facing applications. Some CDR vendors

provide additional tooling that allows developers to easily

browse the data repositories and build queries. Although

there are some open-source CDRs available, they do not

have any current equivalent tools.

Key Features:

Write AQL queries to return health records from CDRs.

Execute AQL queries onto multiple CDRs at once.

Create a single federated table of results from multiple

CDRs.

Easy-to-use GUI built with Electron.

Upload templates found online to multiple CDRs at

once.

University College London | Systems Engineering 2018/2019 | Team 27 | Christian Martin Rios, Daniel Min, Leo McArdle

Working on:• Displaying results in a more user-friendly

manner.

• List templates of multiple CDRs.

• Session-based authentication system on

CDRs.

• Federating results of AQL statements from

CDRs.

Health-023-19

IXN – Exploration of 3D clinical models manipulation

Samuel Bouilloud, Yue Wu, Chris Obasi / Client: Ben Park - Sopra Steria

Key Features

The ability to use gestures for natural

interaction with 3Dmodels inMixed Reality

Guidelines for gestures and manipulation of

objects inMixed Reality

Ability to strip layers of the 3D models, and

place markers on them

Ability to use haptics for a better

interaction experience

Our Technologies

Evaluation

● All key functionalities have been implemented,

but a lot can be done in terms of performances

and advanced features

Future Work

● The integration with the HoloLens 2

● A full working “co-op” mode, with users

manipulating the same objects in real time

The Main Screen in Mixed Reality, where the

user can manipulate the object via gestures

(move, rotate, zoom, etc); or enter the Menu to

choose an object / access the documentation.

The Documentation for each gesture, with some

explanation and videos. A training mode and

tutorial are also available, for the user to get

used to the different devices.

Abstract

This project, in collaboration with Sopra Steria and Great Ormond

Street Hospital, is about creating a way for surgeons at GOSH to

be able to simulate operations. The goal here is to increase

security and efficiency of medical interventions. In addition,

medical students could also use it, as a tool to learn and test their

skills/knowledge.

We aim to integrate the Microsoft HoloLens with the LeapMotion

(hand-tracking device) and the Buru-Navi (device which guides the

user with vibrations) to manipulate 3D organ objects in Mixed

Reality.

We successfully integrated the HoloLens with the devices,

allowing users to naturally manipulate different 3D models, and

being able to place markers, find them using the Buru-Navi, or

even strip layers of the objects. In addition, a full documentation is

available, including a tutorial and a training mode. Health-024-19

Health-024-19

HoloHandClient: KazendiTeam 33: Farid El-Aouadi, Cyrus Horban, Sehej Sethi

Abstract

HoloHand is a mixed reality project

using the Microsoft HoloLens. Using the"research mode" we have made

breakthroughs in live hand trackingusing the HoloLens. The application

intends to deliver an experience whichwalks the users through the hand

segmentation process, allowing them touse finger gestures to paint in

augmented reality.

Key Features

Access Data Streams in the Microsoft HoloLens.

A clear UX design to walk the user through the hand segmentation process.

The functionality to allow users to

use finger gestures to paint in augmented reality.

Hand Tracking Using

HoloLens

Augmented

Reality Finger

Painting

Key RequirementsBold text implies the "must have" requirements

• Ability to access data sensors• Track individual fingers• Overlay stylised hand

instructions• Painting using finger gestures

Health-025-19

TitleMedical AR Object RecognitionCOMP0016 Team 34

Joseph Halse

Duncan Rowe

Xuanwei Chen

AbstractThere is a lack of hands-free technology in the health sector. To combat

this issue, NTT Data has decided to create a hands-free prototype that

uses machine learning and augmented reality to provide instructions to

the user. While wearing Epson smart glasses, our machine learning

algorithm will identify key medical objects and pull up instructions on

how to use these objects. The user will be able to choose which

instructions to view by taking advantage of the voice-control option

allowing them to work on the machinery while viewing the instructions.

User

friendly

Time

saving Interactive

Hands

free

Informative

Real

time object

recognition

Voice

recognition

Health-026-19

AR Portal for GOSH Patients

Client: NTTData, GOSH

Team member: Yin Long Ho, Chirag Hedge, Haonan Zhang

Customizable Augmented Reality Experience

Abstract

The aim of the project is to help reassure

young patients before they have an

operation. Children are more often afraid of

the unknown, hence our solution is made to

help children familiarise themselves with the

medical environment beforehand.

Our project consists of two parts: a web

application where users can customise

augmented reality (AR) rooms, and a mobile

application where the patients can enter an

AR portal and then explore the

aforementioned rooms, whilst interacting

with objects. Both applications are designed

to be easy to use for users without any

technical background.Key Features

- Web app

- Customize AR room and fill it with objects.

- Add object interactions.

- Public/private sharing option.

- Authentication system.

- Mobile app

- Place AR Portal in the real world.

- Enter the AR world by walking through the portal.

- Drag and move objects around.

- Play 360-degree video.

Enter the generated code for

the room in our mobile app

Walk into the AR portal and

explore the room in real

world.

Future work

Whilst our applications are made in mind of

the healthcare sector, our completed project

is a generic design that can be built upon in

the future, in various industries and sectors,

such as for training new staff and leisure

activities.

Health-027-19

HoloLens to Support Clinical Teaching and Training

AbstractWe have created a HoloLens application to assist medical

students and trainees with their education by facilitating mixed reality. Our application is able to extract medical terminology while a senior doctor speaks and provides immediate

information in front of the junior doctors eyes without distracting them with extra screens.

Our Solution• Create immersive three-dimensional content using

Unity and the HoloToolkit• Extract medical terms with the NLP Lexigram API

and provide explanations and diagrams of terms

• Show graphs and patient data from JSON files by utilising the Mixed Reality experience of the HoloLens

Interactive medical application for HoloLens

Real-time extraction of medical terminology

Customisable UI using gestures, voice control and spatial understanding

Key Requirements

Voice commands for navigation and interaction

Natural Language Processing extracting medical terminology from speech

Holographic display of data and User Interface according to Mixed Reality best practices

Key Features

Patient medical data retrieval from files to HoloLens interactive experience

Team 7

Petros Xenofontos, Elena Aleksieva, Amy JeffcoateClients

DRIVE (GOSH), NTTData, Microsoft

Health-028-19

WELLWELLWELLWellbeing Monitoring App

University College London | Team 9 | Zuka Murvanidze, Nanxi Zhang, Azizan Wazir | 2018/2019

Historically, there has been no way of accurately tracking the mental wellbeing of

patients. Triage and resource allocation is difficult as a result of this lack of information,

and as such, the NHS Wellbeing App was designed to collect data in a non-intrusive

and subtle manner.

WellWellWell is an app that tracks a user’s phone usage statistics to determine their

wellbeing following an approved NHS framework - the 5 Ways to Wellbeing. The app

tracks the user’s pedometer data (number of steps taken), social media and other

telecommunication app usage to generate a wellbeing score, which is indicative of how

closely the user is following the NHS framework.

Key Requirements:Project Abstract:

UK Wellbeing data visualized according to postcode area addresses

Compatible with 95%

of Android devices

SQLite for local data

management

MySQL nationwide database to

store anonymously shared data

Node.Js back end for the web

app that processes and

visualizes the shared data

Using ML.NET and Tensorflow

to predict wellbeing scores

Home page

Data processing and

classification, data sharing

StatisticsGenerated PDFLive Monitoring

Key Features and Technologies:

Full anonymity of all user

shared information

System Architecture

Design Machine Learning

model to predict well being scores

Manage data locally to give

users full control of their data

Create user friendly android

application for data gathering and sharing

Enable data sharing via

generated documents

Design network for

anonymous data sharing and give an user option to opt in or

out

Display user history

comparing current to previous data

Data sharing via pdf and

txt documents

Health-029-19

IXN – CRF Staff Activity Tracker

Great Ormond Street Hospital + DRIVE

Team 17: Darren Ko & Jake Currant

GOSH Clinical Trial employees currently employ basic excel spreadsheets

to log and analyse staff activity data over designated time intervals.

This suffers from inaccurate data collection and low user satisfaction

stemming from inefficient logging processes and being constrained to PC

desktop-reliant data input. The project improves and streamlines the

process by moving data collection to mobile devices. Erroneous user input

is greatly reduced and activity logging is made more

efficient as minimal user interaction is required to input data.

Simultaneously, the project improves support of management staff

workflows, with data handling and analytics output delivered via a

companion web page for administrators’ and business managers’

ease of use.

Abstract Technological Solution

Mobile App (Ionic)

Web Client (HTML, CSS)

Web Server (Django) Database (MySQL)

Requirements and Implementation

Administrative Staff

• Assign specific bands of staff individual

activities

• Generate analytics and information

procedurally, and export them

• Manage and oversee use across all user

types

End Users

• Enter activity data on the go as fast as

possible, according to trial and time spent

• Manage all allotted tracking days

• Ensure ease of use with help features and

minimal required input

• Implement reminders and notifications

In association with Lorraine Hodson, Gemma Molyneux, Daiana Bassi, Christy Rowley

Supervisor: Dr Yun Fu

Health-030-19

A disease is classified as rare if it occurs in fewer than 5 in

10,000 people. Due to the low incidence, research on rare

diseases is limited and it is hard for doctors to identify them due to

the lack of precedence cases. 75% of rare diseases affectchildren and 30% of patients will die before their fifth birthday. To

improve that situation, in 2015 GOSH opened the world’s first

research centre for rare diseases in children. Our project aims to

build on that research to create a comprehensive educational

platform that allows trainee doctors to view anonymized clinicalobservations of patients with rare diseases. The platform will

improve their knowledge and training experience and prepare

them better to identify and recognize rare diseases in the future.

Rare Disease Repository

Team 03: David Elston, Georgi Krastev, Max von Borch | Client: GOSH | Supervisor: Dr. Yun Fu London, March 2019

Key Features

Account creation only

allowed for NHS

approved domains

Registered doctors can

upload cases of rare

diseases

Administrator will check

cases for anonymity

and correctness

Approved cases can be

accessed by public

Registered users can

bookmark cases for

later use

Registered users can

contact case authors

for questions or

recommendation

Solution

Web-based application developed with python

and django

Back-end hosted on Microsoft Azure using

PostgreSQL as a

database

Responsive UI with NHS look and feel implement-

ted using HTML,

Bootstrap and CSS

Requirements

Intuitive functionality and ease of use

Guarantee anonymity of medical data due to

data protection

Case authors can upload media files

along cases

Abstract

Health-031-19

Client

Musgrove Park Hospital is part of Taunton and Somerset

NHS Foundation Trust. It is a successful District General

Hospital, the largest in Somerset; serves a population of

over 340,000 and with an excellent reputation for

providing a comprehensive range of medical, surgical and

specialist services.

Background

The Pre-Operative Assessment Clinic (POAC) Working Group is looking to utilise a digital

platform to screen patients prior to their operation to identify those that are more

complex and who may need a POAC appointment/multi-disciplinary team (MDT)

involvement.

Requirements

Health Assessment – assessment questions

History – patients can retrieve previous answers

Profile – patients can modify personal details

Help – FAQs

Feedback– patients’ feedback on the app

View Patient – users can search for patients’

responses

Assessment Dashboard – summary results from

the assessment

Feedback Dashboard - summary results from the

patient feedback

User Management – administrators can edit user

details and deactivate users

Question Management – users can modify the

questions

System Architecture

Mobile

App Website

Database

ServerHTTP requests

and responses

HTTP requests

and responses

SQL

Team 09Jonathan Choi | Sheng-Wen Huang |Nishchal Sen

IXN – Pre-Operative Assessment Clinic

Health-032-19

Health-001-19

INDUSTRY EXCHANGE NETWORK

Visitor Management App

Students: Benjamin Smith, Chao Ding (Team 8)

Company: Great Ormond Street Hospital, DRIVE

Clients: Gemma Molyneux, Daiana Bassi

Features

Ionic App

• During an event, visitors who have pre-booked can search for their

details in a list that is dynamically filtered based on text input.

• During normal office hours, visitors can quickly input their details into

the system so that they are stored whilst the visitor is in the building.

• Admin users can access the web admin panel or fire checklist from

the main page of the app.

Web App

• A list of current events, upcoming events or past events can be seen

and these are ordered by date and time.

• Clicking on an event in this list allows a user to see its details, modify

them and delete or close the event.

• New events can be added using a button on the same screen.

• A list of the visitors that are currently signed into the building can also

be accessed so that their details can be retrieved.

• Event or visitor data can be uploaded into the system or exported to

an email address in a .csv file.

• During an evacuation, visitors that are currently in the building can be

signed off on a fire checklist. This is persistent across all devices, so

multiple members of staff can check people off at the same time.

AbstractGOSH DRIVE regularly host events in their office space, such as seminars for IT and healthcare professionals. In

addition, a number of other visitors enter the unit during normal office hours. Currently however, management of

both event visitors and drop-in visitors is done using physical records. Therefore, it would be beneficial if this

process could be digitised. Digitalisation will improve the visitor sign in experience and staff can more readily

access event and visitor data when required. The goal of this visitor management app has therefore been to

increase the efficiency of the sign in procedure for visitors, as well as give the staff at DRIVE an easier way of

accessing and manipulating this event/visitor data. The system we have created involves four main components: an

Ionic app, which the visitors can use to sign in/out of the building; a web admin panel that allows staff members to

view, modify or export the data in the database; a node.js backend and a MySQL database (both of which are

hosted in the Microsoft Azure Cloud).

UI Design

Ionic App

Visitor Sign-out Page

Web App

Events List Page

System• Client-side Ionic app

and web interface.

• Node.js back-end on

NGINX server in

Azure Cloud.

• MySQL database

server also in Azure

Cloud.

Health-002-19

INDUSTRY EXCHANGE

NETWORK

Abstract

This project involves prototyping, designing and developing an

application, which will allow the Taunton and Somerset NHS Foundation

Trust to triage the patients to an appropriate specialist or specialists. This

would, in turn, ensure a higher efficiency and consequently shorter

waiting-times for patients to be assigned to either a dietitian, an

endocrinologist or another specialist where appropriate. The application,

apart from allowing the patients to track their weight, mood and other

factors is ensure that the patients are triaged efficiently, which will reducethe waiting time and thus provide a higher quality service.

Tech Behind

Admin Page

Server Database

Architecture

The project contains one mobile app

for patients and one admin website

for the management team. We use

node.JS to transfer the data between

the front-end of the application and

the database. The back-end is

deployed on Microsoft Azure.

COMP0067: Design (2018/19)Organisation: Taunton and Somerset NHS Foundation Trust

Client: Marie Little

Team 18: Damian Harateh, Wentian Fang, Demilson Fayika

Admin Website

Requirements

1. Will allow users to enter information about their diet.

2. Present a set of questions to the patient in relation to their

diet to determine the level of care they require.

3. Requires patients to enter their login details to use the app.

4. The app will provide doctors an update with information

about the patients’ health and medical questionnaire responses.

Phone Features:

Team 16 – Wilfrid Berry, Cecilia Pretus, Poyzan TaneliSupervisors – Dr Yun Fu (UCL), Daiana Bassi (GOSH Drive)

GOSH Drive Device Management System

The Key Features: Website & App

The Requirements

For the app:

• Use QR codes for easy check in/out and manual option

• Enter client details at check out

• Send confirmation emails to client and GOSH

For the website:

• Ability to bulk import device data

• Generate QR codes

• Add or remove devices

Abstract

“GOSH Drive is the new unit of Great Ormond Street Hospital that aims to

enhance the use of technology in healthcare, digitalise and transform the

existing systems to improve patient outcomes”(1).

Our client has a number of Samsung devices they lend out to students and other individuals. Manually managing and keeping track of the different devices can get messy. Our client wants a way to automate the management for the devices they lend out. We decided that the best solution was to create a mobile app to check-in/out devices and a separate website to allow bulk imports and manage all devices.

The Solution

• An easy to use app with a clean design to quickly check in/out devices, with the option to scan a QR code or enter manually.

• A flexible and customisable website with an easy to navigate dashboard that makes adding, removing and tracking devices easy and convenient. Select check in/out manually

or with QR codeEnter client detailsTrack and manage devices

from dashboard

The Technology

MySQL Database

PHP REST API

Azure Web ServerClient Browser (CSS, HTML, PHP)

Android App(Ionic 4)

(1) GOSH Drive Website

Health-003-19

Abstract

Often within medical research, clinical trials and studies are

required to test or evaluate new drugs against performance

requirements. The process of doing so currently involves

recruiting potential trial participants, and providing them with

information sheets to read and consent forms to fill out,

which are filed away, in case of the need to retrieve at a later

date. With our solution we aim to automate this process by

creating a web application that allows those authorised, to

create studies and relevant information sheets, and manage

the medical staff who have access to such studies and

information. We also aim to build a mobile app that allows

medical staff to present the information sheets and consent

forms to prospective participants live, allowing for information

sheets to be sent to participants email addresses, and for

signatures of consent to be stored in a remote database.

Evaluation

The implementation of the front-end pages for web application and the mobile app have been done using HTML and CSS, the mobile app done specifically using the Ionic Framework.

Addition of dynamic elements and behaviour such as, creating certain page actions given a certain button click, has been implemented using PHP. PHP allows for the addition of dynamic contents to the application that was created in HTML and embedding PHP into the static code is can be achieved more easily than other back end scripting languages. Furthermore, PHP is compatible with multipleplatforms and provides fast outputs, both of which are necessary features given the nature of the application.

The creation of the mobile app has been done using the ionic as aforementioned. Wireframes and basic designs had initially been made on Ionic Creator, hence made the transition to prototype more seamless. Ionic also allows for standalone UI design and creation without access to back-end solutions and therefore was a sensible choice.

The database management system used MySQL. It was chosen because of its good security profile as well as its capability for integration with Azure which we are using to host and deploy our applications.Thus far, we have created the web application pages that allow an authorised member of staff to create accounts for other medical staff and manage their access to certain studies. We have also created pages for the mobile application that allow frontline users to show forms that were created by authorised users on the web application

Future workFor the web application, we hope to augment the ability to create a form for a study, with interactive drag and drop features such as a text box, tick box,

smiley face (for children) and ability to add images to the form. For the mobile application we aim to finalise the ability to save participant signatures given on a mobile device, as a PDF to a remote database.

INDUSTRY EXCHANGE NETWORK

Informed Consent App

GOSH Drive

Dan Ward | Ross Murray | Azariah Kusi-Yeboah

Daiana Bassi | Gemma Molyneux

Client Browser (HTML, CSS, JavaScript

Web Server (PHP) Database (MySQL)

Health-004-19

UCL internal code

Abstract

Our client is entering the clinical trial phase of a new treatment for Osteoradionecrosis; a rare side effect that develops some time after radiation therapy has ended. It usually occurs in the lower jaw, or mandible. We provide a solution that will run concurrently with the clinical trial. The mobile client will allow collection of this data, previously done by a research nurse which is costly and impractical for patients. A Web Client provides admin services for the trial, allowing the set up, maintenance and data extraction from the trial.

INDUSTRY EXCHANGE NETWORK

RAPTOR – Patient feedback solution

Team 19: Samin Ahbab, Shan Pandya, Yusuf Sohoye

Client: Dr. Richard ShawSupervisor: Dr Yun Fu

Logos from company

and technologies used

Mobile Client

• Cross platform mobile app that allows patients to record their symptoms (Pain,

Swelling, Eating)

• Push Notifications to remind users to take the

survey every 10 days.

• Accessibility, varied education level and English speaking ability.

• Minimal time cost, the survey can be

completed in under 30 seconds

• Secured using a unique Trial ID and

authentication code for each user

• Documentation for information about the

current trial, always available.

Web Client

We developed a custom frontend to create a

bespoke solution to manage the dataflow of the

client. This was written in React and is a fully

responsive bootstrap led design. This handles the

needs of the nurse, and smooths business

processes for the NHS trial.

Backend

Node.js and mySQL is used at the backend.

A robust safety netting protocol has also been

designed which automatically flags up patients to

doctors if necessary.

Security considerations have been made and proper

authentication is required to access any routes on the server

Health-005-19

UCL internal code

Abstract

A web portal that allows nurses to easily conduct annual health questionnaires with CIC (Children in Care). Children can select skills that they themselves are interested in developing, and establish a plan with the nurse on how best to achieve their goals.

CIC can also track their progress of these goals over time, allowing for genuine personal development.

INDUSTRY EXCHANGE NETWORK

IXN – Looked after Children Health Plan

Company - North Tyneside CCG

Team - Christopher Pettinga, Nilayraj Patel, Anthony Williams

Client - Marc Rice

Setting personalised goalsCreate sense of achievement and self-

efficacy through progress tracking

Simple ranking system

Results easily shared with relevant peopleQuestionnaire design

Client computer Python (Flask) backend Amazon AWS RDS instance

Health-005-19