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TRUVACE RECORD VERSION
record: TRV-2026-2390
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-06T00:02:42.901344Z
status: published
lens: trace
sector: health
headline: External validation of a deep-learning algorithm for perimetric glaucoma detection on color fundus photographs in the Rotterdam Study
dek: 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…
gain_title: 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.
problem_title: 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.
trace_subject: Deep-learning glaucoma screening on color fundus photographs in the Rotterdam Study
gain_reading: 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.
gain_evidence: Correctly identified cases were frequently flagged at an earlier examination, years before the perimetric diagnosis
problem_reading: 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.
problem_evidence: this resulted in 5.1 referrals per detected glaucoma case | Incomplete ONH visibility was an important source of false positives
quick_read: Researchers tested a deep-learning algorithm that won the AIROGS glaucoma screening challenge on a new population: 19,088 eyes from 9,837 Rotterdam Study participants aged 50-80. Of those eyes, 201 (1.1%) had perimetric glaucoma. At 95% specificity, the algorithm detected 70.6% of affected eyes, with an ROC-AUC of 0.911.

At participant level, sensitivity was 68.8% and ROC-AUC was 0.909. The algorithm had been trained on 101,442 macula-centered photographs, yet it performed no worse on disc-centered images. Correctly identified cases were often flagged years before the perimetric diagnosis.

The screening cost is substantial: 5.1 referrals for each detected case, with a precision-recall AUC of 0.343 because glaucoma is rare in this population. The authors conclude that fundus-photo deep learning could serve as a scalable triage tool, and that image acquisition standards matter for real-world use.
limitation: 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.
tag: Dual reading
key_points: The validation covered 19,088 eyes from 9,837 participants in three Rotterdam Study cohorts, all aged 50-80. | Only 201 eyes (1.1%) had perimetric glaucoma, so positive predictive value was limited, with a PR-AUC of 0.343. | Eye-level sensitivity at 95% specificity was 70.6% (95% CI 63.8-76.8); participant-level sensitivity was 68.8%. | True positives had more advanced visual field damage than false negatives (median mean deviation -10.8 vs -5.8 dB). | The algorithm showed no loss of performance on disc-centered photographs despite training only on macula-centered ones. | Incomplete optic nerve head visibility was an important source of false positives. | The overall screening yield was 5.1 referrals per detected glaucoma case.
rundown: The study applied the AIROGS challenge-winning algorithm, built to detect referable presumed perimetric glaucoma, to participants in a prospective population-based cohort. Performance was assessed at eye and participant level with cluster bootstrap confidence intervals, and misclassified cases were reviewed qualitatively.

Results showed ROC-AUC values near 0.91 at both levels, but sensitivity of about 70% at 95% specificity. Cases the algorithm caught had worse visual field loss (median mean deviation -10.8 dB) than those it missed (-5.8 dB), so milder disease was more likely to slip through.

Further analyses compared macula- and disc-centered photographs in pairs, tracked detection over time using Kaplan-Meier analysis, and examined how image quality affected positive predictive value. The authors say their findings point to image acquisition standards as key to implementation.
sources:
- peer_reviewed | Ophthalmology | https://doi.org/10.1016/j.ophtha.2026.10.030 | 2026-11-02
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