Journal article
Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes
DSW Ting, CYL Cheung, G Lim, GSW Tan, ND Quang, A Gan, H Hamzah, R Garcia-Franco, IYS Yeo, SY Lee, EYM Wong, C Sabanayagam, M Baskaran, F Ibrahim, NC Tan, EA Finkelstein, EL Lamoureux, IY Wong, NM Bressler, S Sivaprasad Show all
JAMA Journal of the American Medical Association | Published : 2017
Abstract
IMPORTANCE: A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. OBJECTIVE: To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. DESIGN, SETTING, AND PARTICIPANTS: Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detectingdiabetic retinopathy (using 76 370 images), possible glaucoma (125189 images), and AMD (72 61..
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Awarded by School of Medicine, Johns Hopkins University
Funding Acknowledgements
This project received funding from National Medical Research Council (NMRC), Ministry of Health (MOH), Singapore National Health Innovation Center (NHIC), Innovation to Develop Grant (NHIC-I2D-1409022); SingHealth Foundation Research Grant (SHF/FG648S/2015), and the Tanoto Foundation; unrestricted donations to the Retina Division, Johns Hopkins University School of Medicine. For the Singapore Epidemiology of Eye Diseases (SEED) study, we received funding from NMRC, MOH (grants 0796/2003, IRG07nov013, IRG09nov014, STaR/0003/2008 and STaR/2013; CG/SERI/2010) and Biomedical Research Council (grants 08/1/35/19/550 and 09/1/35/19/616). The Singapore Diabetic Retinopathy Program received funding from the MOH, Singapore (grants AIC/RPDD/SIDRP/SERI/FY2013/0018 and AIC/HPD/FY2016/0912).