An Explainable Multimodal Deep Learning Framework for Glaucoma Detection and Progression Prediction Using Optical Coherence Tomography and Visual Field Data

Glaucoma is a leading cause of irreversible blindness, and timely detection and monitoring are essential to prevent permanent visual loss. Artificial intelligence (AI) has shown strong potential for automated diagnosis, but progression prediction remains a more challenging and clinically significant task. This study proposes an explainable multimodal deep learning framework for both glaucoma detection and progression prediction using the Harvard Glaucoma Detection and Progression dataset. The framework integrates specific modality encoders for optical coherence tomography B-scans, retinal nerve fiber layer thickness (RNFLT) maps, and visual field (VF) features through a feature fusion module. For the glaucoma detection task, the multimodal model achieved near-perfect performance with an AUROC of 0.9989 and a sensitivity of 0.9944. For the more complex progression prediction task, the full-fusion model achieved an AUROC of 0.7836. Using an optimized validation threshold, the model provided a balanced clinical operating point with reasonable sensitivity and specificity. Ablation experiments revealed a complementary relationship between modalities; VF features provided the strongest standalone ranking signal, RNFLT maps contributed the highest specificity, and multimodal fusion yielded the best overall precision–recall performance, although not the highest AUROC. Gradient-weighted Class Activation Mapping analysis indicated that the model focused on clinically plausible localized retinal regions. These findings support explainable and multimodal AI for glaucoma progression-risk identification.