Prediction of structural glaucoma progression from baseline fundus photographs using deep learning: a retrospective multicentre study

Abstract

Background: Identifying patients with glaucoma who are at risk of rapid disease progression is crucial to preventing vision loss. We aimed to develop and externally validate G-PROG, a deep learning model that predicts 2–5-year glaucoma progression from baseline colour fundus photographs (CFPs). Methods: G-PROG was trained and validated on data from a single centre (UZ Leuven, Leuven, Belgium); the other datasets (Brussels, Belgium; Liège, Belgium; Tampere, Finland; Mainz, Germany; and Hangzhou, China) served as external test sets. Across six glaucoma departments, we analysed 161 827 fundus images from 127 962 visits (13 913 patients), totalling 128 021 eye-years of follow-up. Progression was defined by the G-RISK slope, calculated via within-eye linear regression on longitudinal G-RISK predictions over follow-up intervals of 2–5 years. G-RISK is a previously validated deep learning model that quantifies glaucomatous optic nerve damage from CFPs. We trained 20 G-PROG configurations with varying inclusion criteria applied to the number of visits, image quality, time between visits, and G-RISK at baseline. Performance was evaluated using the area under the receiver operating characteristic curve (AUC), the coefficient of determination (R2), and explained variance score (EVS). G-RISK slope as a progression biomarker was validated against the visual field mean deviation (MD) slope and average retinal nerve fibre layer thickness (RNFL) slope. Findings: Significant AUC values were obtained in 18 out of 20 model configurations, with internal validation reaching a maximum AUC of 0·98 (95% CI 0·97–1·00) across follow-up intervals (2–5 years). In glaucomatous eyes with a baseline G-RISK exceeding 0·6, the maximum AUC was 0·92 (0·85–0·98). For external validation, the predictions from the eight top-performing configurations (selected based on positive R2 and minimal discrepancy between R2 and EVS in internal validation) were averaged. Maximum AUC values ranged from 0·74 to 0·86 across the five test datasets. G-RISK slope showed significant agreement with established progression markers, with maximum AUCs of 0·82 for MD slope and 1·00 for average RNFL slope. Interpretation: Externally validated across five international cohorts, G-PROG predicts 2–5-year glaucoma progression from baseline CFPs. Prospective evaluation is warranted to assess whether G-PROG can improve risk stratification and resource allocation in glaucoma care. Funding: This work was funded and supported by grants from the National Medical Research Council, National Research Foundation Singapore, National Health Innovation Centre Singapore, SingHealth and Duke-NUS, Duke-NUS, the Singapore Eye Research Institute and Nanyang Technological University and the Singapore Eye Research Institute, the Competitive Research Funding of the Pirkanmaa Wellbeing Services County, the LUX–Foundation for Glaucoma Research, state funding for university-level health research at Tampere University Hospital, Wellbeing Services County of Pirkanmaa, the Tampere University Hospital Support Foundation, and the Belgian Ophthalmology Cooperation in Clinical Sciences initiative hosted by the Funds for Research in Ophthalmology.