Explainable Multimodal OCT and OCTA Analysis Reveals Structural and Vascular Features of Mild Glaucoma Across...

To develop and externally validate an explainable artificial intelligence model integrating optical coherence tomography (OCT) and OCT angiography (OCTA) for mild primary open-angle glaucoma (POAG) and to determine the structural and vascular features contributing to classification across ethnically distinct cohorts.

This cross-sectional, multicenter diagnostic study included participants with mild POAG (visual field mean deviation ≥ -6 dB) and normal controls. Quantitative structural OCT and vascular OCTA features from the optic disc and macula were extracted. An Asian cohort (n = 621; 429 glaucoma, 192 controls) was used for model development and testing, and an independent Caucasian cohort (n = 131; 62 glaucoma, 69 controls) was used for external validation. Random Forest models using OCT-only, OCTA-only, and combined OCT/OCTA features were compared using area under the receiver operating characteristic (AUC) curve, sensitivity, and specificity. Feature contributions were assessed using SHapley Additive exPlanations (SHAP).

Among 752 participants (mean age, 60 years; 50% male), the multimodal model achieved high discrimination in the internal test set (AUC = 0.975) and comparable performance to OCT-only model (AUC = 0.965; P = 0.248) but outperformed the OCTA-only model (AUC = 0.870; P < 0.001). In the external test set, the multimodal model achieved an AUC of 0.884, outperforming both OCT-only (AUC = 0.801; P < 0.001) and OCTA-only (AUC = 0.792; P = 0.005) models. SHAP analysis identified inferior peripapillary retinal nerve fiber layer thickness and peripapillary perfusion density as key contributors.

Explainable multimodal OCT/OCTA analysis identified consistent structural and vascular features of mild glaucoma across ethnically distinct cohorts. OCTA-derived vascular metrics provided complementary information to structural OCT, particularly in the external validation cohort.