Multimodal Deep Learning for Longitudinal Prediction of Glaucoma Progression Using Sequential RNFL, Visual Field, and Clinical Data

Forecasting glaucoma progression remains a major challenge in preventing irreversible vision loss. We developed and validated a multimodal, longitudinal deep learning framework to predict future progression using a large retrospective cohort of 10,864 patients from Mass Eye and Ear. The model integrates sequential structural (OCT RNFL scans), functional (visual-field maps), and clinical data from a two-year observation window to forecast progression over the subsequent two-to four-year horizon. Four backbone architectures (ConvNeXt-V2, ViT, MobileNet-V2, EfficientNet-B0) were coupled with a bidirectional LSTM to capture temporal dynamics. The ConvNeXt-V2-based model achieved 0.97 AUC and 0.94–0.96 accuracy, outperforming other backbones with robust performance across sex and race subgroups and only modest attenuation in those > 70 years. Saliency maps localized to clinically relevant arcuate bundles, supporting biological plausibility. By effectively fusing multimodal data over time, this framework enables accurate, interpretable, and equitable long-horizon risk stratification, advancing personalized glaucoma management.