Sector: HealthCare

Team Name: Support Vector Humans

Team Members: Soumyadeep Mukherjee, J H M Apoorva, Pratik Agarwal, Abhinandan Dubey, Rahul Sharma, Otkrist Gupta, Mrinal Mohit, Sai Sri Sathya, Pratik Shah

Challenge: Accessible Healthcare to Billions by providing rapid screening layer between patient and doctor.

Root-Causes:

Lack of Medical Expertise, Experience:

Today there is dearth of doctors and specialists in India as pointed out by WHO standards as 1 doctor for every 1700 patients whereas the minimum ratio is 1:1000. At times of crisis, the patients are asked to shift to higher level facilities due to lack of expertise and experience of doctors leading to several casualties.

Low Speed of Diagnosis:

Due to lack of preliminary screening layer for patients, doctors today are not able to prioritize among critical and not so critical patients. Further they spend majority of the time looking into diagnosis reports which can be automated than treating the patients.

Lack of Accessibility:

Besides shortage of doctors, their unwillingness to work in the rural areas is another issue. It creates artificial scarcity in one area and high concentration in another. Hence the situation further worsens in rural, semi-urban areas.

Lack of Standard Framework:

The knowledge transfer among medical fraternity is mostly verbal and a standard framework for sharing methodologies, new findings, creating a dataset does not exist. In today’s data driven world, there is a high need to establish a reference framework, dataset for doctors to collaborate, obtain trends and make future predictions.

Field Trips:

As part of the field trip, we met Dr. Sirish (ENT Specialist), Dr. Roma Bagi (Dentist) and obtained valuable suggestions for our platform.

Interaction with Dr. Sirish: He used the web interface annotation tool for the ear. He helped us identify the features that are of interest to the ENT specialists in the dataset we had, and also shared his experience as a user of the software platform which will help us update our platform for a perfect user experience. Another important aspect of the conversation was the challenges that he mentioned are currently faced by doctors in his field, which can be eliminated to a large extent with the deployment of the automated platform.

Interaction with Dr. Roma Bagi: We showed her the segmentation tool for automatic segmentation of plaque affected regions in the mouth and the datasets we have from the Kumbh Mela which can be used to diagnose caries, gingivitis etc. She made some

important remarks about the kind of information that can be extracted from these datasets and the role of a doctor in validating any abnormalities that the segmentation tool might detect. She acknowledged the utility of the diagnostic platform as a support system for the doctors to monitor the progress of a patient.

Solution: To use machine learning over large scale datasets to build automatic screening, detection and diagnostic tools for doctors, device makers and patients.

Approach:

The accessibility of data pertaining to various organs, vital signs such as blood pressure, motion sensor data and patient's previous history are to be captured and assessed to provide accurate diagnosis. Hence Dataset Collection is a crucial aspect. The collected data will be in the raw form and needs to be accompanied with annotations or diagnostic reports to make sense of it. In cases where the diagnostic reports are not available, the data has to be labelled as healthy or unhealthy by providing comments regarding the state of the condition. Hence a user friendly Data Annotation interface has to be developed to aid the doctors to label, annotate the data in efficient, hassle-free manner. Once labelled data is available Machine Learning Models can be trained to provide automatic screening, detection and diagnostic tools.

Addressing Root-Causes:

Lack of Medical Expertise, Experience:

Automated tools need much lesser or no human intervention once respective machine learning models are trained successfully. Hence they fill in for lack of medical expertise. Also with such tools in place, junior level assistants can be efficiently trained by providing various possible examples in the dataset and their corresponding diagnosis.

Low Speed of Diagnosis:

By providing a rapid screening layer between doctors and patients, preliminary decisions can be provided by automated tools. The doctor can further assess the condition of the patient according to the criticality and give more time, attention towards treating and checking the progress of their health condition. All in all, the doctor's throughput will be increased with the help of automated tools.

Lack of Accessibility:

Today with emerging mobile health care devices, a preliminary diagnosis of the patients can be done at rural areas via health camps. With automated tools in place, immediate diagnosis can be provided and the critical patients can be channelized to specialists for specific diagnosis and treatment.

Lack of Standard Framework:

Obtaining a diverse dataset to build a generic model in very essential. And a standard

framework will aid the process where the data is obtained via secured channels, protocols. A diverse dataset will act as a reference to the doctors in their practice and they can also share their methodologies as to how they tackled specific issues.

Proof of Concept:

Data Collection:

Data pertaining to 500 patients was collected during KumbhMela 2015 using mobile health care devices. AliveCor was used to record ECG, Sopro Care for oral imaging, Cellscope for Ear imaging, D-EYE for retinal imaging, Kinect Sensors for motion tracking, Vital signs, Patient's medical history and demographics were collected via questionnaires.

Data Annotation and Labelling:

Doctor’s expertise is required in converting the raw data to a labelled dataset. And towards this end, user friendly interfaces were built over the course of the camp specific to obtained datasets.

Ear, Eye Imaging:

Videos pertaining to Ear were focused on ear drum and Eye videos covered the retinal area. An annotation tool as a web interface was developed to allow the doctor to play, pause the patient's video and annotate the important areas by labelling them as healthy and unhealthy with added comments. Additionally replay option, list of annotations were displayed to review their annotations.

Oral Imaging:

Videos of the interior of the mouth were taken in 3 different light conditions which mark caries (blue light) and gingivitis (yellow light). A segmentation tool was developed which automatically segments out these regions with the additional feature that gives the doctor the ability to mark any regions that might have not been detected by the program and annotate them.

ECG Data:

ECG graphs were obtained from AliveCor as pdf files and a processing tool was developed to obtain numeric data from them.

Motion Tracking:

The body movements such as a subject walking in a straight line, touching his nose or opening a jar act aid in determining neurological symptoms. A screening tool was developed where the patient's video was displayed and annotation options were provided to label the data.

Vital Signs:

Vitals contained Galvanic Skin Response of the body for 10 sec and Blood Pressure, Heart Rate, Oxygen Saturation, Body Temperature, Height and Weight. The GKR was plotted and other vital signs were tabulated to asses.

Stakeholders:

Device Makers: The medical devices deployed in diagnostic centres, hospitals are mostly highly expensive due to the high end research, prototyping and testing involved in developing them. A diverse dataset will benefit them in developing robust devices and screening, detection, diagnosis tools can be plugged into their devices.

Government: The dataset created via the rapid screening layer, sources from various doctors will aid in analysis of trends and making predictions. Automatic Diagnosis from Mobile Health care will enable government health agencies and NGO's to conduct low cost, effective health camps in rural areas making health accessible to the remote areas as well.

Doctors: The throughput of the doctors will be significantly increased with the help of rapid screening layer meanwhile helping them prioritizing the critical vs not so critical patients. Such a system will help them train the junior level doctors and provide a standard framework wherein they can contribute to knowledge transfer and also learn new findings in their respective fields.

Patients: In terms of value being added, the patients will be a major stakeholder in obtaining available, accessible and affordable health care.

Future Work:

During the 7 days of the workshop, several annotation, processing tools were developed and we received several valuable suggestions, reviews from the specialists regarding the user interface design. Over the course of coming 6 months, a further iteration in incorporating the suggestions will be taken up. With annotation tools in place, datasets will be labelled by expert physicians and machine learning models will be trained for screening, detecting, automating diagnosis. The end-to-end system would involve several iterations of the same process to ensure accuracy of the models. We aim to test the developed models, get reviewed and establish the proof of concept for the future deployment of the platform at a larger scale.

With accurate models in place, they will be deployed by collaborating with Device Manufactures, Hospitals and Government. The current dataset involves data from mobile health care devices and the solution will be further scaled up to high end image scanning systems such as CT scan, MRI etc. A futuristic system would be a health kiosk which will scan the entire body and give automatic diagnosis.