A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations. Issue 6 (June 2020)

• Record Type: Journal Article
• Authors: Sabanayagam, Charumathi; Xu, Dejiang; Ting, Daniel S W; Nusinovici, Simon; Banu, Riswana; Hamzah, Haslina; Lim, Cynthia; Tham, Yih-Chung; Cheung, Carol Y; Tai, E Shyong; Wang, Ya Xing; Jonas, Jost B; Cheng, Ching-Yu; Lee, Mong Li; Hsu, Wynne; Wong, Tien Y
• Is Part Of: Lancet. Volume 2:Issue 6(2020)
• Page Start: e295
• Page End: e302
• Publication Date: 2020-06
• Subjects: Medical care -- Data processing -- Periodicals; Medical care -- Information technology -- Periodicals; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/S2589-7500(20)30063-7
• Languages: English
• ISSNs: 2589-7500

Abstract

Summary: 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 1·73m 2 . 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.