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

Abstract 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. Competing Interest Statement The authors have declared no competing interest. Funding Statement This research was funded by the National Institutes of Health (NIH: R01 EY036222 and R21 EY035298), and MIT-MGB AI Cures Grant. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board at Mass General Brigham (MGB) approved this study. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present study are available upon reasonable request to the authors.