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Enhancements to a computer-assisted screening technology for diabetic retinopathy: system redesign based on our pilot study in indian setting

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Authors

Sheila John, Kulasekaran S, Supriti M, Keerthi Ram, Mohanasankar S, Rajiv Raman,

Badrinath S.S

Sankara Nethralaya

Healthcare Technology Innovation Centre, IIT Madras

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Diabetic Retinopathy (DR) in India

More than 60 million diabetic people in India Prevalence of DR is 18% in diabetic population Significant prevalence in both rural and urban population

Acute shortfall of ophthalmologists1 per 100,000 population

Need: Preventive eye-care through early identification

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Diabetic Retinopathy Screening Model

Ophthalmologist - Based Model Ophthalmologist - Led Model

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Screening technology for DR

Existing computer-assisted DR screening solutionsEurope: UK: iGrading, Portugal: RetmarkerAmericas:US: IDx-DR, Canada: CARAState of the art performance: sensitivity 97% at

47% specificity †

† Retinal imaging and image analysis, Abramoff et al, IEEE rev. Biomed. Engg, 2010

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Screening technology for DR

India Academic research activities at IIT-KGP, IIIT-Hyd, IIT-Madras, few Engg. Colleges

DR screening research activities world-over more than 200 peer-reviewed publications since 2003

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IITM DR screening system

Normal anatomy detection Clinical signs detection

Red lesion detectionOptic disc and

macula detection

Blood vessel segmentation

Bright lesion detection

Small red dots detection

Image gradabilityInput image

Analytics

DR Referral decision

Grading system based on International Clinical Diabetic retinopathy Disease Severity Scale (ICDR) 5 severity levels

Normal anatomy detection Clinical signs detection

Red lesion detectionOptic disc and

macula detection

Blood vessel segmentation

Bright lesion detection

Small red dots detection

Image gradabilityInput image

Analytics

DR Referral decision

Grading system based on International Clinical Diabetic retinopathy Disease Severity Scale (ICDR) 5 severity levels

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IIT Madras DR screening system

Consists of modules for detecting disease signs, and analytics for providing a referable vs non-referable decision

Developed and benchmarked using 2000 publicly available fundus images acquired in clinical settingsRefinements to algorithms for working in Indian settings : 85.9% sensitivity at 83% specificity

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Pilot retrospective study and observations

Observed performance on subset of 200 images of SN-DREAMS Retrospective study

Mydriatic, 45 degree Retinal imagesIncludes images with media opacity, severe

pathology, and lower quality of image capture, for observing performance

Grading by ophthalmologist following ICDR guideline – 5 severity levels

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Pilot retrospective study and observations

Needs to handle image gradability and non-mydriatic imaging

Separate analytics for diabetic macular edema and Proliferative diabetic retinopathy

Designed to find new cases of DR, but also Laser treated cases.

Evaluation of inter-observer variability and consensus should be carried out

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Module for image gradabilityImage

preprocessing

Enhanced image

Structure distribution

Colour distribution

Contrast Illumination SNRHomogeneity Moments

Quality prediction

Gradability score

Reference images for good gradability

Reference images for poor gradability

Quality parameters

Retinal image

Evaluated on 240 images: 82% sensitivity at 80% specificity

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Redesign: module for CSME

Includes module for accurate localization of macula and optic disc resilient to presence of disease signs

Detection of Hard exudates, cotton-wool spots, and identification of circinate

clusters

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Process flow of the proposed DME grading method

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Evaluated on 587 images: Sensitivity of 90%

International Clinical Diabetic Macular Edema Disease Severity Scale

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Redesign: Module for Proliferative DR

Diabetes Proliferative

DR (PDR)Non-proliferative DR

(NPDR)

• Neovascularization • Vitreous hemorrhage• Retinal detachment

Pre-proliferative DR / Severe NPDR

Vision loss

• Microaneurysms• Small hemorrhages• Exudates

• Macular edema • Ischemic regions, …

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Module for proliferative Diabetic Retinopathy

Module developed with heat map to identify new vessels /Proliferative vascular abnormality and retinal detachment

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Module developed for PDR identification

Image-level decision of PDR presence

Divide image into non-overlapping

uniform size patches

Characterize local texture

Derive vascular morphometric

features

Compute a sensitive

vessel map

For each patch

Dense descriptor of

patch

Recognize neovascularity

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Proliferative Diabetic Retinopathy

Evaluated on 1052 imagesSensitivity: 85.6% at specificity of 97.3%

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Summary

Since diabetic macular edema accounts for more than 40% of all DR related vision loss, special

module was developed and evaluated

Misdiagnosis of late stage DR is highly unfavorable and associated vision loss, so detection of PDR was developed, identifying NVE, NVD, Fibro vascular proliferation and retinal

detachment

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Intelligent identification of image gradability is necessary for the other modules to be effective, so gradability module was developed

Algorithm - good sensitivity and specificity to detect presence or absence of DR

Cost effective large scale screening of diabetic patients to prevent blindness in the

population

Summary

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