aceExternal validation of a deep-learning algorithm for perimetric glaucoma detection on color fundus photographs in the Rotterdam Study
Purpose To externally validate a deep-learning (DL) algorithm detecting perimetric glaucoma on color fundus photographs (CFPs) in a population-based cohort. Design External validation in a prospective population-based cohort. Participants Participants aged 50-80 years from three Rotterdam Study cohorts. Methods We applied the winning Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge algorithm, trained on 101,442 macula-centered CFPs to detect referable presumed perimetric glaucoma. Perform…
In a population study of 19,088 eyes, a deep-learning algorithm detected 70.6% of eyes with perimetric glaucoma at 95% specificity, often flagging cases years before diagnosis.
The same algorithm needed 5.1 referrals per detected glaucoma case, missed less advanced cases, and produced false positives when the optic nerve head was only partly visible.
Glaucoma was rare in the cohort (1.1% of eyes), giving a modest PR-AUC of 0.343, and incomplete optic nerve head visibility caused many false positives.
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- Peer-reviewedOphthalmology2026-11-02
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Truvace Impact Record TRV-2026-2390, v1: “External validation of a deep-learning algorithm for perimetric glaucoma detection on color fundus photographs in the Rotterdam Study.” Truvace, 2026-11-06. /record/TRV-2026-2390 (accessed on the day you copy it). sha256 717d63b82b762824…
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