Deep Learning in Population-Based Screening for Glaucoma.

BACKGROUND/AIMS:Deep learning algorithms have shown promise for glaucoma detection using retinal imaging. The Northern Finland Birth Cohort Eye Study is a randomised prospective cohort designed to evaluate the efficacy and cost-effectiveness of glaucoma screening in a middle-aged population. This study assessed the performance and generalisability of G-RISK, a deep learning model for glaucoma screening from fundus images, in individuals aged 45-49 years with low glaucoma prevalence (1.1%). METHODS:Four screening scenarios were evaluated: detection of (1) glaucoma eyes, (2) glaucoma patients, (3) glaucoma and glaucoma suspect eyes and (4) glaucoma and glaucoma suspect patients. Performance across four photographic modalities was assessed using the area under the receiver operating characteristic curve (AUC), the precision-recall AUC (PR-AUC), and sensitivity at 95% specificity. RESULTS:G-RISK achieved its highest performance when screening for glaucoma eyes using colour fundus photographs, with an AUC of 0.83 (95% CI 0.75 to 0.90), PR-AUC of 0.16 (95% CI 0.05 to 0.28) and sensitivity at 95% specificity of 0.43 (95% CI 0.28 to 0.59). Furthermore, discrimination remained consistent across other modalities (AUC 0.75-0.78). Performance was higher for confirmed glaucoma than combined glaucoma and glaucoma suspect cases (AUC 0.83 vs 0.67). CONCLUSIONS:G-RISK achieved the best results on colour fundus images, while maintaining discrimination across other imaging modalities. Screening metrics were constrained by the relatively young age and low glaucoma prevalence of the cohort. The reduced performance observed when including glaucoma suspects may relate to variability and uncertainty in their clinical definition.