Dr. Praveen Kumar B.A. M.B.B.S, M.D. presents artificial intelligence in healthcare at the OpenPOWER Summit Europe, October 2018.

1. Artificial Intelligence (AI) in Healthcare
OpenPOWER Academia Team
PES Institute of Medical Sciences and Research, Kuppam, Andhra
Pradesh, India.
Dr. Praveen Kumar B.A. M.B.B.S., M.D

2. Overview
1. Background
 Current Health Scenario - India
 AI - Scope & potential in the Healthcare
2. Our rural health facility
 Priority area and sub areas
 Proof of concept and interim findings
 Deliverables and potential impact
3. Way forward

3. Health Scenario: India
• Triple burden of Disease
• Rural-Urban Health divide
• Doctor-Population ratio
• Specialist care at remote areas
• Health Infrastructure
• Digital push by Government of
India
• Universal Health coverage
(Ayushman Bharat)

4. AI in Healthcare: Indian Scenario
• AI has the potential
to address the
lacunae of
healthcare industry.

5. AI Applications in Healthcare
• Detection
• Diagnosis
• Prediction
• Drug Discovery
• Personalized Medicine
• Medical Imaging
• Genomics
• Cancer Research
• Brain Tumors
• Dermatology
• Mental Health
• Speech Patterns
• Diabetes
• Radiology

6. PES Institute of Medical Sciences and Research (PESIMSR),
Kuppam, South India.
• Rural tertiary care teaching institute
• Tri state junction of Southern India catering to
around 1 million rural population (120 km)
• NABL & NABH accredited
• OP, IP, ICU and 24x7 Trauma & Emergency care
• 750 bedded hospital

7. OpenPower activity to medical community
@PESIMSR, Kuppam, India

8. Burning issues in our facility and the scope for
AI
1. Diabetic Retinopathy diagnosis in rural outreach activity.
2. Emergency patient care at the facility.
3. Early diagnosis of life-threatening illness in children
4. Diagnosis and managing endemic disease e.g. Malaria
5. Chronic diseases (E.g. Diabetes Mellitus, Hypertension)

9. 1. Diagnostic Tool: Diabetic Retinopathy (DR) in rural
outreach activity.
Customized model being created using the Deep Learning approach,
to execute the Algorithm on IBM POWER9TM Platform.
• High resolution image date set has been taken with around 35000
images which are labeled under 5 categories
0 - No DR
1 - Mild
2 - Moderate
3 - Severe
4 - Proliferative DR
• Preprocessing of data happened in which images are rotated to 90,
180, 270 degrees to make the dataset more accurate for training,
extended dataset has around 100,000 images. Diabetic Retinopathy using AI.mp4
ImageforrepresentationSource:JofEngineeringMedicine

10. • Deep learning algorithm has been created on Power9
platform and data is now ported to POWER9 system for
training, training is done using Single AC922 Server
• Once the training is completed, it generates a model as an
output.
• Generated model could be ported on any commodity
device, mobile device or any device based on OS,
Alternatively model could also be deployed on cloud service
which could inference from local system by uploading the
image.

11. • Retina image capturing device captures the image and send the
image to cloud or the local device which runs the model, It also
sends images with marking label to Power9 system which will
further enhance the model based on new images.
• If the Retina capturing device is a portable device working in
remote areas with no internet connectivity, we can design a new
device to adapt the model on the hardware and do the inferencing
on new images captured offline.
• A model for reference is deployed on Power9 and inference could
be seen at
https://github.com/arshad2101/retinopathy/blob/master/Retinopa
thy.ipynb.

12. Deployable
Model Flow
1.DR High
Resolution
Labeled
Image set
3.POWER9
System model
training
2.Image
Preprocessing
and Data
Augmentation
4.Trained
Model
5.Inferencing
output from
the trained
model
6.Retina
Image
Capturing
Device
Feed to
train
the
model
again
with
new
images
Inferencing could be done on cloud, sending image
to cloud or model could itself be deployed on
mobile device to do the inference

13. 2. Assistive tool in Emergency patient care
• CT scan image analysis and assistive tool for triage priority:
• Scope of Project
To detect and classify: a) Infarcts b) Bleeds c) Fractures
• Method: Deep Learning techniques and Image Processing
tools and algorithms detection of abnormalities in the CT
Scan images. The analysis will include the possible region
of the abnormality and the possible emergency level.
Helpful in assessing & predicting patient prognosis and in
early care.

14. 2. Proposed Model of Deployment
1. Patient Admitted
Patient is admitted to
the hospital in the
emergency ward
Patient Details are
entered in the
Hospital
Management
Information System
Backbone
2. Need for
CT scan
Identified
Initial treatment is
done
The Radiology
Department is
intimated of the
patient details and
CT Scan
3. CT Scan is
Performed
CT Scan is
completed and the
images are
uploaded on the
Hospital
Management
Central Cloud
Server
5. Triage:
Intimation to
Radiologists
If any abnormality
is seen in the
scans, the
concerned
radiologist is
intimated to look at
the case as a high
priority.
4. IMAGE
PROCESSING
AND
DIAGNOSIS
CT Images are
processed and
updated images
with initial
diagnosis are
uploaded on the
Central Cloud
Server.
Images Axial 5 m.m. Series. The module is a hybrid network with independent pre-processing for each type of defect

15. Divides the images into 4 classes:
1.Bleeds 2.Infarcts 3.Fractures 4.Normal
Results based on preliminary analysis
Confidence:
Bleed: 78%
Infarcts: 21%
Normal: 1%
Confidence:
Bleed: 82%
Infarcts: 16%
Normal: 2 %
Interim Results

16. Interim Results
The result that are obtained by our algorithm shows an
average case accuracy of 72% for the four classes.
Actual results based on preliminary analysis
Image Processing
and Classification
Model deployed on
the POWER System
32 images set of Axial 5m.m. cuts Image with marked abnormalities
Confidence:
Fracture: 98%
Normal: 2 %

17. 3. Laboratory diagnostic tool in Acute infections
• Diagnosis of Dengue fever cases and neonatal sepsis by
analysing images of blood smear and haematological
profile.
• Scope of Project: To identify pattern and diagnose
a) Dengue fever b) Neonatal sepsis
• Method: Image processing and pattern learning
capabilities of neural networks is used to analyse the
haematological parameters and peripheral smear images
of patients. Integrating these analytics with patients’
clinical findings in assisting patient diagnosis.

18. 3. Proposed Model of Deployment
1. Patient Blood
Smear Sampling
Patient blood smear
is sampled
2. Smear
observation
Blood Smear
sample is
observed under
microscope
integrated with
digital camera
3. Image
Uploading
Images captured
of the blood smear
are uploaded on
the Central Cloud
Server
5. Cross
Validation of
results
Results can be
cross-validated
with
haematological
profile to get faster
and reliable
diagnosis of acute
infections
4. IMAGE
PROCESSING
AND
DIAGNOSIS
Blood Smear
Images are
processed and
updated images
with initial
diagnosis are
uploaded on the
Central Cloud
Server.

19. 5. Diagnostic tool for Tropical/Endemic
disease
• Malarial parasite detection and characterization
• Plasmodium Falciparum/Vivax/Ovale/Malariae
Thick and Thin Blood smear examination
Imageforrepresentationsource:https://en.wikipedia.org/wiki/Blood_film

20. Characterization of Species
Source: Department of Microbiology, PESIMSR, Kuppam. Image for representation Source: Journal of Medical Systems

21. 6. Prediction tool for Chronic diseases management
• Diabetes Mellitus
and Hypertension
• Helps in prediction
and prevention of
complication
• Case based
reasoning cycle
Source: Artificial Intelligence for Diabetes Management and Decision Support: Literature Review
J Med Internet Res 2018;20(5):e10775

22. Deliverables & Expected Impact
• Building a feasible & robust end to end AI model which can
be deployable in the current setting.
• Assist the health team in quick diagnosis and providing
quality care to patients
• Provide a research platform for the medical and technical
team to explore health technology development and
implementation.
• Scalable solutions to a wider reach
• Huge impact in this rural area

23. Way forward…
• Increasing the size of standardized data to further train the
model
• Networking with the academia and industry
• Technology support
• Funding

24. Acknowledgements
• Ganesan Narayanaswamy, IBM.
OpenPower Leader for Education & Research
• Mohamad Arshad, IBM.
Data Scientist.
• Sourabh K and Aditya M, Scholars, PICT, Pune.
• PESIMSR Health Technology Research Group, Kuppam.

25. Thank You
drpraveenba@gmail.com
drpraveenba@pesimsr.pes.edu
+91 9494071558