A generalizable deep learning regression model for automated glaucoma screening from fundus images.

ABSTRACT: A plethora of classification models for the detection of glaucoma from fundus images have been proposed in recent years. Often trained with data from a single glaucoma clinic, they report impressive performance on internal test sets, but tend to struggle in generalizing to external sets. This performance drop can be attributed to data shifts in glaucoma prevalence, fundus camera, and the definition of glaucoma ground truth. In this study, we confirm that a previously described regression network for glaucoma referral (G-RISK) obtains excellent results in a variety of challenging settings. Thirteen different data sources of labeled fundus images were utilized. The data sources include two large population cohorts (Australian Blue Mountains Eye Study, BMES and German Gutenberg Health Study,

SUBMITTER: Hemelings R 

PROVIDER: S-EPMC10264390 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature