Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142447
Title: Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy : a multi-ethnic study
Authors: Ting, Daniel S. W.
Cheung, Carol Y.
Nguyen, Quang
Sabanayagam, Charumathi
Lim, Gilbert
Lim, Zhan Wei
Tan, Gavin S. W.
Soh, Yu Qiang
Schmetterer, Leopold
Wang, Ya Xing
Jonas, Jost B.
Varma, Rohit
Lee, Mong Li
Hsu, Wynne
Lamoureux, Ecosse
Cheng, Ching-Yu
Wong, Tien Yin
Keywords: Science::Medicine
Issue Date: 2019
Source: Ting, D. S. W., Cheung, C. Y., Nguyen, Q., Sabanayagam, C., Lim, G., Lim, Z. W., . . . Wong, T. Y. (2019). Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy : a multi-ethnic study. npj Digital Medicine, 2(1), 24-. doi:10.1038/s41746-019-0097-x
Journal: npj Digital Medicine
Abstract: In any community, the key to understanding the burden of a specific condition is to conduct an epidemiological study. The deep learning system (DLS) recently showed promising diagnostic performance for diabetic retinopathy (DR). This study aims to use DLS as the grading tool, instead of human assessors, to determine the prevalence and the systemic cardiovascular risk factors for DR on fundus photographs, in patients with diabetes. This is a multi-ethnic (5 races), multi-site (8 datasets from Singapore, USA, Hong Kong, China and Australia), cross-sectional study involving 18,912 patients (n = 93,293 images). We compared these results and the time taken for DR assessment by DLS versus 17 human assessors – 10 retinal specialists/ophthalmologists and 7 professional graders). The estimation of DR prevalence between DLS and human assessors is comparable for any DR, referable DR and vision–threatening DR (VTDR) (Human assessors: 15.9, 6.5% and 4.1%; DLS: 16.1%, 6.4%, 3.7%). Both assessment methods identified similar risk factors (with comparable AUCs), including younger age, longer diabetes duration, increased HbA1c and systolic blood pressure, for any DR, referable DR and VTDR (p > 0.05). The total time taken for DLS to evaluate DR from 93,293 fundus photographs was ~1 month compared to 2 years for human assessors. In conclusion, the prevalence and systemic risk factors for DR in multi-ethnic population could be determined accurately using a DLS, in significantly less time than human assessors. This study highlights the potential use of AI for future epidemiology or clinical trials for DR grading in the global communities.
URI: https://hdl.handle.net/10356/142447
ISSN: 2398-6352
DOI: 10.1038/s41746-019-0097-x
Rights: © 2019 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles

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