ArticleEye (London, England)2026
Clinically aligned artificial intelligence for glaucoma diagnosis: enhancing retinal nerve fibre layer interpretation from fundus images.
Article in Eye (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial intelligence is here to stay: the role of scientific etiquette.Eye (London, England) · 2026Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesThis study aimed to develop an interpretable artificial intelligence (AI) screening system that replicates a specialist's evaluation of fundus photographs. The system analyses three signs: visible retinal nerve fibre layer (RNFL) defects (as observed on fundus photography, not OCT-based measurement), vertical cup-to-disc ratio (VCDR) and rim-to-disc ratio (RDR). SUBJECTS/
methodsA total of 773 fundus images from the independent test cohort were annotated by three fellowship-trained glaucoma specialists, followed by repeated consensus meetings to improve annotation consistency. Two models were trained on the development cohort: an EfficientNet-B4 classifier for RNFL and a U-Net with an EfficientNet-B4 encoder for optic disc/cup (OD/OC) segmentation. During the testing phase, a referral was triggered whenever a feature was classified as abnormal, with the system explicitly reporting the specific sign that prompted the referral decision.
resultsAmong the 773 expert-annotated cases, 749 were included in the analysis after excluding low-quality or insufficient-information images (AI drop rate: 3.1%). The final independent test cohort comprised 268 referral and 481 non-referral cases, providing a basis for evaluation. The combined model achieved a sensitivity of 0.903 (95% confidence interval [CI], 0.868-0.938), specificity of 0.821 (95% CI, 0.787-0.855) and a positive predictive value (PPV) of 0.738 (95% CI, 0.690-0.785). The system captured complementary aspects of glaucomatous optic neuropathy that are often missed by single-feature approaches.
conclusionsConsensus-based annotation, combined with lesion-level modelling, enhances alignment with clinical reasoning. By providing an explicit referral rationale, the system fosters trust in AI-assisted glaucoma screening and facilitates adoption in clinical settings.
Identifiers
42493574What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.