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

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

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.

Authors: Ruben Hemelings, Damon W Wong, Jacqueline Chua, Jan Van Eijgen, João Barbosa-Breda, Adèle Ehongo, Nathalie Collignon, Stefan Kiekens, Simon C König, Bart Elen, Bingyao Tan, Gerhard Garhöfer, George Barbastathis, Hannele Uusitalo-Järvinen, Alexander K Schuster, Tin Aung, Anja Tuulonen, Ingeborg Stalmans, Leopold Schmetterer

Link: https://www.thelancet.com/journals/landig/article/PIIS2589-7500(26)00055-5/fulltext

Doi: 10.1016/j.landig.2026.101032

NGP Papers Manager: Carlo Cutolo