AI-Driven Multimodal Prediction of 10-Year Incident Glaucoma Integrating Genetic and Deep Learning–Derived Imaging Features

June 1, 2026Ophthalmology ScienceOpen Access

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Key Points

This research aims to predict the 10-year risk of primary open-angle glaucoma (POAG) using a multimodal machine learning framework.
Population-based prospective cohort study using UK Biobank data with 340 incident POAG cases and 9,374 controls.
Developed a machine learning framework integrating deep learning imaging features, genetic data, and clinical factors.
Utilized SHapley Additive exPlanations for model interpretability and validation of influential predictors.
XGBoost model achieved AUC of 0.927 (95% CI, 0.895-0.959) for incident POAG prediction, with sensitivity of 0.685 at 0.95 specificity.
PRS and DL-derived features provided additional predictive value over traditional clinical factors.
Top predictors included CFP DL score, age, PRS, and intraocular pressure, maintaining model performance even with reduced feature sets.

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Population-based prospective cohort study predicts incident glaucoma using genetic and imaging data, suggesting early intervention potential.

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Wu et al. (2026) studied this question.

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