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Artificial Intelligence Using Deep Learning to Screen for Referable and Vision-Threatening Diabetic Retinopathy in Africa

35 Pages Posted: 30 Jan 2019

See all articles by Valentina Bellemo

Valentina Bellemo

Singapore National Eye Centre

Zhan W Lim

National University of Singapore (NUS)

Gilbert Lim

National University of Singapore (NUS)

Quang D Nguyen

Singapore National Eye Centre

Yuchen Xie

Singapore National Eye Centre

Michelle YT Yip

Duke-NUS Graduate Medical School Singapore

Haslina Hamzah

Singapore National Eye Centre

Jinyi Ho

Singapore National Eye Centre

Xin Q Lee

Singapore National Eye Centre

Wynne Hsu

National University of Singapore (NUS)

Mong L Lee

National University of Singapore (NUS)

Lillian Musonda

Kitwe Central Eye Hospital

Manju Chandran

Frimley Park Hospital

Grace Chipalo-Mutati

Lusaka University Teaching Hospital

Mulenga Muma

University of Zambia (UNZA)

Gavin SW Tan

Singapore National Eye Centre

Sobha Sivaprasad

NIHR Moorfields Biomedical Research Centre

Geeta Menon

Frimley Park Hospital

Tien Y Wong

Singapore National Eye Centre

Daniel Shu Wei Ting

Singapore National Eye Centre - Artificial Intelligence for Ophthalmology

More...

Abstract

Background: Radical measures are required to identify and reduce blindness due to diabetes to achieve Sustainable Development Goals by 2030. Therefore, we evaluated the accuracy of artificial intelligence (AI) using deep learning in a population based diabetic retinopathy (DR) screening program in Zambia, a lower middle-income country, ranked 159th in terms of gross domestic product.  

Methods: A total of 4504 retinal fundus images from 3093 eyes of 1574 Zambians with diabetes were prospectively recruited. Referable DR was defined as moderate non-proliferative DR (NPDR) or worse, diabetic macular edema (DME) and ungradable images. Vision-threatening DR (VTDR) included severe NPDR and proliferative DR. We calculated the area under curve (AUC), sensitivity and specificity for referable DR, and sensitivities of VTDR and DME, using an Ensemble convolutional neural network compared to the grading by retinal specialists. A multi-variate analysis was performed for systemic risk factors and referable DR between AI and human graders.

Findings: The prevalence of referable DR, VTDR and DME was 22·5%, 5·5% and 8·1% respectively. The AUC of the AI system for referable DR was 0·973, with corresponding sensitivity of 92·25% and specificity of 89·04%. VTDR sensitivity was 99·42% and DME sensitivity was 97·19%. AI model and human graders demonstrated similar outcomes in referable DR prevalence detection and systemic risk factors associations. Both AI model and human graders identified longer duration of diabetes, higher level of HbA1c, and increased systolic blood pressure as risk factors associated with referable DR.  

Interpretation: An AI system shows clinically acceptable performance in detecting referable DR, VTDR, and DME in population-based DR screening, demonstrating the potential application and adoption of such AI technology in the under-resourced African population to reduce the incidence of preventable blindness.

Funding Statement: This project received funding from National Medical Research Council (NMRC) Health Service Research Grant, Ministry of Health (MOH), Singapore (National Health Innovation Center, Innovation to Develop Grant (NHIC-I2D-1409022); SingHealth Foundation Research Grant (SHF/FG648S/2015), and the Tanoto Foundation. The Singapore Diabetic Retinopathy Program (SiDRP) received funding from the MOH, Singapore (grants AIC/RPDD/SIDRP/SERI/FY2013/0018 & AIC/HPD/FY2016/0912).

Declaration of Interests: Drs Ting, Lim, Lee, Hsu and Wong are co-inventors of a patent on the deep learning system in this paper; potential conflicts of interests are managed according to institutional policies of the Singapore Health System (SingHealth) and the National University of Singapore (NUS).

Ethics Approval Statement: Lewis et al. DR screening program was previously approved by Topical Diseases Research Centre (TDRC), Ndola, Republic of Zambia. Subsequently, this specific study was also approved by the Centralized Institutional Review Board (IRB) of SingHealth, Singapore (protocol number SHF/FG648S/2015) and conducted in accordance with the Declaration of Helsinki.

Keywords: Diabetic Retinopathy, Zambia, Developing Country, Telemedicine, Artificial Intelligence, Deep Learning

Suggested Citation

Bellemo, Valentina and Lim, Zhan W and Lim, Gilbert and Nguyen, Quang D and Xie, Yuchen and Yip, Michelle YT and Hamzah, Haslina and Ho, Jinyi and Lee, Xin Q and Hsu, Wynne and Lee, Mong L and Musonda, Lillian and Chandran, Manju and Chipalo-Mutati, Grace and Muma, Mulenga and Tan, Gavin SW and Sivaprasad, Sobha and Menon, Geeta and Wong, Tien Y and Ting, Daniel Shu Wei, Artificial Intelligence Using Deep Learning to Screen for Referable and Vision-Threatening Diabetic Retinopathy in Africa (January 25, 2019). Available at SSRN: https://ssrn.com/abstract=3324738

Valentina Bellemo

Singapore National Eye Centre

Singapore

Zhan W Lim

National University of Singapore (NUS)

Bukit Timah Road 469 G
Singapore, 117591
Singapore

Gilbert Lim

National University of Singapore (NUS)

Bukit Timah Road 469 G
Singapore, 117591
Singapore

Quang D Nguyen

Singapore National Eye Centre

Singapore

Yuchen Xie

Singapore National Eye Centre

Singapore

Michelle YT Yip

Duke-NUS Graduate Medical School Singapore

Singapore
Singapore

Haslina Hamzah

Singapore National Eye Centre

Singapore

Jinyi Ho

Singapore National Eye Centre

Singapore

Xin Q Lee

Singapore National Eye Centre

Singapore

Wynne Hsu

National University of Singapore (NUS)

Bukit Timah Road 469 G
Singapore, 117591
Singapore

Mong L Lee

National University of Singapore (NUS)

Bukit Timah Road 469 G
Singapore, 117591
Singapore

Lillian Musonda

Kitwe Central Eye Hospital

Kitwe
Zambia

Manju Chandran

Frimley Park Hospital

Frimley
United Kingdom

Grace Chipalo-Mutati

Lusaka University Teaching Hospital

Zambia

Mulenga Muma

University of Zambia (UNZA)

Zambia

Gavin SW Tan

Singapore National Eye Centre

Singapore

Sobha Sivaprasad

NIHR Moorfields Biomedical Research Centre

London
United Kingdom

Geeta Menon

Frimley Park Hospital

Frimley
United Kingdom

Tien Y Wong

Singapore National Eye Centre

Singapore

Daniel Shu Wei Ting (Contact Author)

Singapore National Eye Centre - Artificial Intelligence for Ophthalmology ( email )

Singapore

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