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Detecting Glaucoma in the Ocular Hypertension Treatment Study Using Deep Learning: Implications for clinical trial endpoints

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posted on 19.07.2021, 15:27 by Rui FanRui Fan, Christopher Bowd, Mark Christopher, Nicole Brye, James A. Proudfoot, Jasmin Rezapour, Akram Belghith, Michael H. Goldbaum, Benton Chuter, Christopher A. Girkin, massimo faziomassimo fazio, Jeffrey M. Liebmann, Robert N. Weinreb, Mae O. Gordon, Michael A. Kass, David Kriegman, Linda M. Zangwill

To investigate the diagnostic accuracy of deep learning (DL) algorithms to detect primary open-angle glaucoma (POAG) trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS). 66,715 photographs from 3,272 eyes were used to train and test a ResNet-50 model to detect the OHTS Endpoint Committee POAG determination based on optic disc (n=287 eyes, 3,502 photographs) and/or visual field (n=198 eyes, 2,300 visual fields) changes. OHTS training, validation and testing sets were randomly determined using an 85-5-10 percentage split by subject. Three independent test sets were used to estimate the generalizability of the model: UCSD Diagnostic Innovations in Glaucoma Study (DIGS, USA), ACRIMA (Spain) and Large-scale Attention-based Glaucoma (LAG, China). The DL model achieved an AUROC (95% CI) of 0.88 (0.82, 0.92) for the overall OHTS POAG endpoint. For the OHTS endpoints based on optic disc changes or visual field changes, AUROCs were 0.91 (0.88, 0.94) and 0.86 (0.76, 0.93), respectively. False-positive rates (at 90% specificity) were higher in photographs of eyes that later developed POAG by disc or visual field (19.1%) compared to eyes that did not develop POAG (7.3%) during their OHTS follow-up. The diagnostic accuracy of the DL model developed on the OHTS optic disc endpoint applied to 3 independent datasets was lower with AUROC ranging from 0.74 to 0.79. High diagnostic accuracy of the current model suggests that DL can be used to automate the determination of POAG for clinical trials and management. In addition, the higher false-positive rate in early photographs of eyes that later developed POAG suggests that DL models detected POAG in some eyes earlier than the OHTS Endpoint Committee.

Funding

National Eye Institute R01EY029058, R21EY027945, K99EY030942, R01EY011008, R01EY19869, R01EY027510, R01EY026574

Core Grant P30EY022589

National Center on Minority Health and Health Disparities; Horncrest Foundation; awards to the Department of Ophthalmology and Visual Sciences at Washington University (NIH grants EY09341, EY09307 and NIH Vision Core Grant P30EY02687)

Merck Research Laboratories; Pfizer, Inc.

White House Station (New Jersey)

an unrestricted grant from Research to Prevent Blindness, Inc. (New York, NY)

German Research Foundation research fellowship grant (RE 4155/1-1)

German Ophthalmological Society Grant

History

Email Address of Submitting Author

ranger_fan@outlook.com

Submitting Author's Institution

UCSD

Submitting Author's Country

United States of America

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