A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations

Charumathi Sabanayagam; Dejiang Xu; Daniel S W Ting; Simon Nusinovici; Riswana Banu; Haslina Hamzah; Cynthia Lim; Yih-Chung Tham; Carol Y Cheung; E Shyong Tai; Ya Xing Wang; Jost B Jonas; Ching-Yu Cheng; Mong Li Lee; Wynne Hsu; Tien Y Wong

doi:10.1016/S2589-7500(20)30063-7

A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations

Lancet Digit Health. 2020 Jun;2(6):e295-e302. doi: 10.1016/S2589-7500(20)30063-7. Epub 2020 May 12.

Authors

Affiliations

¹ Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore.
² School of Computing, National University of Singapore, Singapore.
³ Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
⁴ Singapore General Hospital, Singapore.
⁵ Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong.
⁶ Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
⁷ Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing, China.
⁸ Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing, China; Department of Ophthalmology, Medical Faculty Mannheim of the Ruprecht-Karls-University Heidelberg, Mannheim, Germany.
⁹ Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore. Electronic address: ophwty@nus.edu.sg.

PMID: 33328123
DOI: 10.1016/S2589-7500(20)30063-7

Abstract

Background: Screening for chronic kidney disease is a challenge in community and primary care settings, even in high-income countries. We developed an artificial intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal images, which could add to existing chronic kidney disease screening strategies.

Methods: We used data from three population-based, multiethnic, cross-sectional studies in Singapore and China. The Singapore Epidemiology of Eye Diseases study (SEED, patients aged ≥40 years) was used to develop (5188 patients) and validate (1297 patients) the DLA. External testing was done on two independent datasets: the Singapore Prospective Study Program (SP2, 3735 patients aged ≥25 years) and the Beijing Eye Study (BES, 1538 patients aged ≥40 years). Chronic kidney disease was defined as estimated glomerular filtration rate less than 60 mL/min per 1·73m². Three models were trained: 1) image DLA; 2) risk factors (RF) including age, sex, ethnicity, diabetes, and hypertension; and 3) hybrid DLA combining image and RF. Model performances were evaluated using the area under the receiver operating characteristic curve (AUC).

Findings: In the SEED validation dataset, the AUC was 0·911 for image DLA (95% CI 0·886 -0·936), 0·916 for RF (0·891-0·941), and 0·938 for hybrid DLA (0·917-0·959). Corresponding estimates in the SP2 testing dataset were 0·733 for image DLA (95% CI 0·696-0·770), 0·829 for RF (0·797-0·861), and 0·810 for hybrid DLA (0·776-0·844); and in the BES testing dataset estimates were 0·835 for image DLA (0·767-0·903), 0·887 for RF (0·828-0·946), and 0·858 for hybrid DLA (0·794-0·922). AUC estimates were similar in subgroups of people with diabetes (image DLA 0·889 [95% CI 0·850-0·928], RF 0·899 [0·862-0·936], hybrid 0·925 [0·893-0·957]) and hypertension (image DLA 0·889 [95% CI 0·860-0·918], RF 0·889 [0·860-0·918], hybrid 0·918 [0·893-0·943]).

Interpretation: A retinal image DLA shows good performance for estimating chronic kidney disease, underlying the feasibility of using retinal photography as an adjunctive or opportunistic screening tool for chronic kidney disease in community populations.

Funding: National Medical Research Council, Singapore.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
China
Cross-Sectional Studies
Deep Learning*
Eye Diseases / complications*
Eye Diseases / diagnosis
Female
Fundus Oculi
Humans
Image Interpretation, Computer-Assisted / methods*
Male
Middle Aged
Photography / methods*
Renal Insufficiency, Chronic / complications*
Renal Insufficiency, Chronic / diagnosis*
Reproducibility of Results
Sensitivity and Specificity
Singapore