Multimodal foundation model assistance for differentiating primary open-angle glaucoma in highly myopic eyes

To develop and evaluate a multimodal foundation-model-assisted system for differentiating primary open-angle glaucoma (POAG) from non-glaucoma in highly myopic eyes and assessing whether artificial intelligence (AI) assistance changes ophthalmologist diagnostic performance.

Retrospective diagnostic model-development study with internal validation and paired sequential multi-reader evaluation.

Model development used 603 eye-level cases (502 POAG, 101 non-glaucoma); a fixed 150-case set from 149 patients was used for internal test reporting and the paired reader evaluation.

A RETFound-based multimodal classifier used color fundus photographs, optical coherence tomography (OCT) images, and 24 structured OCT/retinal nerve fiber layer (RNFL) variables. Six ophthalmologists reviewed each case without and then with AI assistance after a washout interval.

Model area under the receiver operating characteristic curve (AUC); reader sensitivity, specificity, accuracy, F1 score, confidence, reading time, diagnostic switching, and inter-reader agreement.

The final multimodal model achieved an AUC of 0.911 (95% confidence interval [CI], 0.862-0.950) on the fixed 150-case set. In the sequential paired evaluation, unaided versus AI-assisted mean sensitivity was 68.0% versus 75.3%, accuracy was 74.2% versus 79.6%, F1 score was 0.790 versus 0.844, and mean specificity was 91.3% in both phases; Fleiss kappa was 0.583 versus 0.709. Reader-level diagnostic differences were not significant after multiplicity adjustment and were exploratory.

In this internal retrospective study, the AI-assisted phase showed numerically higher mean sensitivity, accuracy, confidence, and inter-reader agreement than the unaided phase, with unchanged mean specificity. Reader-level diagnostic differences were not statistically significant after multiplicity adjustment and should be considered exploratory. Prospective multicenter validation is required before clinical deployment.