Detecting Glaucoma in the Ocular Hypertension Study Using Deep Learning.

ABSTRACT:

Importance

Automated deep learning (DL) analyses of fundus photographs potentially can reduce the cost and improve the efficiency of reading center assessment of end points in clinical trials.

Objective

To investigate the diagnostic accuracy of DL algorithms trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect primary open-angle glaucoma (POAG).

Design, setting, and participants

In this diagnostic study, 1636 OHTS participants from 22 sites with a mean (range) follow-up of 10.7 (0-14.3) years. A total of 66 715 photographs from 3272 eyes were used to train and test a ResNet-50 model to detect the OHTS Endpoint Committee POAG determination based on optic disc (287 eyes, 3502 photographs) and/or visual field (198 eyes, 2300 visual

SUBMITTER: Fan R 

PROVIDER: S-EPMC8931672 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature