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Susruta:Artificial Intelligence and Bayesian Knowledge Networkin Health Care – Smartphone Apps for diagnosis and

differentiation of anemias with higher accuracy atResource Constrained Point-of-Care settings

The Seventh International Conference onBig Data Analytics

(BDA 2019)

Presented by:Shubham Yadav

BTech-CSEIIIT Naya Raipur

December 18, 2019

The Seventh International Conference on Big Data Analytics (BDA 2019) Presented by: Shubham Yadav BTech-CSE IIIT Naya Raipur (UCLA)Susruta December 18, 2019 1 / 36

Agenda

Introduction

Motivation

Overview

Proposed Solution

Outputs

Major contributions

Future Works

Authors

References


Introduction

Anemia is one of the major public health challenges in the world.

Globally, anemia affects 1.62 billion people, which corresponds to24.8% of the population.

Thalassemia and Sickle cell are two most common genetic disordersthat are life restricting chronic diseases with a presentation of Anemia.

The average prevalence of B-thalassemia carriers in India is about3–4% of the general population.

Thalassemia require long and specialized treatments. They causesevere distress and financial loss to the family.

Unless anemia diagnosis and care is taken to the resource restrictedpoint-of-care, it will impact severely on the overall development of therural population in India.


Motivation (Statistics)


Motivation (Statistics)

It estimated that 24.8% of the global population was anaemicbetween 1993 and 2005. According to WHO Global NutritionReport 2017, 51% of Indian women in reproductive age aresuffering from iron deficiency anemia. During pregnancy, anemia islikely to put both mother and the baby at risk of premature deliveryand low birth weight of the baby – this carries a high risk of stuntedor underdeveloped children.To efficiently deal with anaemia and reduce the mortality rate,periodical monitoring of haemoglobin is necessary for every individual.


Motivation (Indian Population)

Thalassemia screening is today done using HPLC (HighPerformance Liquid Chromatography) that costs between Rupees600 to 1200 and available in selected diagnostic labs in cities

Iron Deficiency Anemia (IDA) tests are invasive and requires trainedhealth workers to collect blood and perform biochemical tests

HPLC Machine Invasive Blood Extraction


Iron Deficiency Anemia and Artificial Intelligence

Anemia can be caused by iron deficiency in the body, which iscommonly known as IDA (Iron Deficiency Anemia)

IDA is treated (or cured) with medicines and change in diet

IDA is also caused by infections that is both preventable and treatable

We developed Artificial Intelligence (Computer Vision and DeepLearning)based non-invasive diagnosis of anemia


Susruta

To reduce the disease burden of both IDA and B-thalassemia trait (B-TT)we have developed Susruta a smartphone based mobile application thatuses 3 major artificial intelligence (AI) components:

Natural language processing (NLP) to analyze the unstructured chiefcomplains and clinical notes.

Artificial Neural Network (ANN) through Machine learning and deeplearning (ML/DL) on 60,286 common blood counts (CBC) dataobtained from a teaching hospital for live B-thalassemia carrierscreening

Image processing and computer vision techniques to interprethemoglobin content in blood through non-invasive analysis ofconjunctiva and nailbed smartphone images.


Susruta

The non-invasive smartphone basedIDA detection system will allow IDAdiagnosis by health workers in interiorunderserved regions at point of care.

Susruta will help arrive at a diagnosisinstantly and accurately through thecombination of diagnostic tests, medicalscoring, and clinical data without anysignificant financial implications forunderserved regions of rural India.


Iron Deficiency Anemia (IDA)


Non-invasive Anemia Screening

Conjunctiva (palpebral and forniceal regions) contains minimalamounts of melanin compared to other parts of the skinFingernail beds contain minimal amounts of melanin compared toother parts of the skinSmartphone pictures of fingernail bed and conjunctiva can be used fornon-invasive, instant, affordable anemia diagnosisImage analysis and Artificial Intelligence applications can detect thehemoglobin content in blood by analyzing these digital smartphonepictures

Conjunctiva Pallor Nail Beds


IDA Functions

A smartphone based App will use thesmartphone camera to take pictures of the eyeconjunctiva and the fingernail pictures

The mobile App uses local store to capture theclinical data and the eye and fingernail picturesbecause there are regions in India whereInternet is not available

The App will analyze the picture and report thehemoglobin count

The data is uploaded and updated in the serverdatabase whenever Internet is available

Features from these conjunctiva and fingerbedimages will be extracted and then combinedwith Clinical information and Fatigue score of aperson to feed an AI (ML/DL) engine todiagnose an IDA case


Add New Records

Demographical informationwith the pictures of eyes andfinger beds are taken

GPS information collectedfor geo-spatial location

Clinical information of theseindividuals are also captured

Fatigue score is computedbased on FACIT standard

The clinical information andfatigue score along with thepictures are stored in thelocal store

These information is used tocalculate the hemoglobincontent


ID Anemia Data (Geo-spatial)


ID Anemia Data (Fatigue Score)


IDA Conjunctiva Cropping


Output Calculation

Figure: Red Intensity in the image

Figure: Green Intensity in the image

EI=log(Sred)-log(Sgreen)The Seventh International Conference on Big Data Analytics (BDA 2019) Presented by: Shubham Yadav BTech-CSE IIIT Naya Raipur (UCLA)Susruta December 18, 2019 17 / 36

IDA Complete Data


Data in Records

The information captured are:

Today’s date: Date of data captureUnique Identifier: May be Aadhar/Voter Id or any other Uniqueidentification information; for a child it may be parent’s IdName of the person: Given name of the personDate of Birth (Age): DOB; age of onset is calculated automaticallyGender: The gender (Sex) of the personOccupation: For socio economic statisticsChief Complains: Clinical informationComorbidity: Clinical information of existing comorbidityDiet pattern: What the individual eats and does not eatFamily History: Any family history that may impact the anemiaAllergy: Any specific allergyRecent Investigations: Clinical symptoms in last 15 daysFatigue Score: Functional Assessment of Chronic Illness Therapy(FACIT) scoreImages: Images of Left Right eyes and Left and Right fingerbeds


Database at Server

The data entered by thehealth worker is storedlocally in the smartphone

This is with theassumption that theInternet may not beavailable for realtimeupdate of the data

Images along with theClinical detail as enteredby the health worker issent to the server usingthe ‘UPDATERECORDS’ in thesmartphone


Thalassemia


Thalassemia, and Artificial Intelligence

Thalassemia belongs to hemoglobinopathy disorders where globinprotein is defective, missing, or nonfunctional

Thalassemia is most common genetic disorder and is chronic

B-thalassemia is quite prevalent in South Asia and in Eastern India

We developed AI (Machine Learning/Deep Learning) drivensmartphone based B-thalassemia screening applications that usesCBC (RBC, Hb, MCV, MCH, RDW) instead of expensive HPLC

We used AI techniques like Natural Language Processing (NLP),Machine/Deep Learning, and Computer vision for the IDA andThalassemia


Thalassemia Data for AI

A Teaching Hospital in Kolkata (Nil Ratan Sircar Medical College andHospital) launched a program for screening of B-thalassemia carriersas part of its Public Health initiative

Over 8 years from 2010 to 2017 this project screened about 60,283individuals in West Bengal

Each person (healthy and thalassemic) examined by experthematologists following HPLC and CBC blood test results

We used this anonymized data to design diagnostic techniques onsmartphones Web using Machine Learning and Deep Learning

This algorithms will use only CBC blood counts to diagnose athalassemia patient

Old blood test reports with old CBC data can be used for screeningas well


B-thalassemia Screening


Healthy Individual

A normal healthy Male datais shown here

The blood count data of aperson is entered into thesmartphone

Values entered are RBC(Red Blood Cell), HB(Hemoglobin), MCV (Meancorpuscular volume), MCH(mean corpuscularhemoglobin), RDW (RedCell Distribution Width)

The CALCULATE buttoncalculates the thalassemiafrom the above blood counts


Continued..

B-thalassemia scoringwas dome using DeepArtificial Intelligencetechnique that usedMachine Learning andDeep Learning

We derived an analyticalalgorithm as well that wenamed TBC.

As a comparison, wehave also used the AI,Mentzer scoring forB-thalassemia

An example for a 19years old Normal Male


B-thalassemia Carrier Case


Continued..


Major Contributions

Thalassemia

There has not been any study previously with such a large populationof 60,283 individual’s CBC data.

We applied AI algorithms on this dataset to bring in speed, accuracy,and efficiency.

Our AI algorithm uses CBC instead of Hb HPLC.

In the process we not only reduced the cost of the screening, but havealso increased the accuracy of the screening more than doublecompared to existing methodologies of analyzing CBCs.


Major Contributions

Iron Deficiency Anemia

In IDA, we had already developed a non-invasive technique of anemiadiagnosis.This system used some ideal laboratory conditions.

In the current system we have enhanced the IDA system to work inresource constrained environment.

Moreover, we added additional features of FACIT and clinicalinformation that increases its accuracy.

This will empower a health worker in rural India to control the burdenof anemia by early diagnosis at the point of care.


Future Works

We further look forward to accumulate more patients data using ourapp in coordination with AIIMS Raipur.

The dataset will be collected keeping the Anaemic/Non-anaemic ratioproper balanced.

Finally develop CNN model trained on the featured dataset which canfinally be used to classify individuals as Anaemic/Non-Anaemic.

Calibrating the module to determine the approximate Haemoglobinlevel apart from the Anaemic/Non-Anaemic label.


Authors

Shubham Yadav1 , Sakthi Ganesh2 , Debanjan Das1 , Venkanna U1

, Rajarshi Mahapatra1 , Ankur Kumar Shrivastava3 , PrantarChakrabarti4 , Asoke K Talukder2*

1DSPM IIIT Naya Raipur, Chhattisgarh, [email protected], [email protected]

[email protected], [email protected] India Pvt. Ltd., Bangalore, India

[email protected], *[email protected] Raipur, Chhattisgarh, India

[email protected] Medical College and Hospital, Kolkata, India

[email protected]


References

[1]Tamir, A., Jahan, C. S., Saif, M. S., Zaman, S. U., Islam, M. M., Khan,A. I., ... Shahnaz, C. (2017, December).Detection of anemia from imageof the anterior conjunctiva of the eye by image processing andthresholding. In Humanitarian Technology Conference (R10-HTC), 2017IEEE Region 10 (pp. 697-701). IEEE.

[2]Roychowdhury, S., Sun, D., Bihis, M., Ren, J., Hage, P., Rahman, H.H. (2017, February).Computer aided detection of anemia-like pallor. InBiomedical Health Informatics (BHI), 2017 IEEE EMBS InternationalConference on (pp. 461-464). IEEE.

[3]Collings, S., Thompson, O., Hirst, E., Goossens, L., George, A.,Weinkove, R. (2016).Non-invasive detection of anaemia using digitalphotographs of the conjunctiva. PloS one, 11(4), e0153286.


[4]Muthalagu, R., Thulasi Bai, V., John, S. (2018).A smart (phone)solution: An effective tool for screening anaemia–correlation withconjunctiva pallor and haemoglobin levels. TAGA Journal, 14, 2611-2621.

[5]Sheth, T. N., Choudhry, N. K., Bowes, M., Detsky, A. S. (1997).relation of conjunctival pallor to the presence of anemia. Journal ofgeneral internal medicine, 12(2), 102-106.

[6]Suner, S., Crawford, G., McMurdy, J., Jay, G. (2007). -invasivedetermination of hemoglobin by digital photography of palpebralconjunctiva. The Journal of emergency medicine, 33(2), 105-111.


[7]Setaro, M., Sparavigna, A. (2002).Quantification of erythema usingdigital camera and computer-based colour image analysis: a multicentrestudy. Skin research and technology, 8(2), 84-88.

[8]Roychowdhury, S., Hage, P., Vasquez, J. (2017). Azure-Based SmartMonitoring System for Anemia-Like Pallor. Future Internet, 9(3), 39.


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