Detecting Glaucoma Across Multi-ethnic Myopic and Non-Myopic Populations Using an Uncertainty-Aware Vision Transformer: A Multicentre Model Development and Validation Study
Organizations: Singapore Eye Research Institute, Singapore National Eye Centre, Singapore · Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore · Institute of Advanced Intelligence and Computing, Agency for Science, Technology and Research, Singapore · Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore · Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine National University of Singapore · Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan · Rothschild Foundation Hospital, Paris, France · Beijing Visual Science and Translational Eye Research Institute (BERI), Beijing Tsinghua Changgung Hospital Eye Center, Tsinghua Medicine, Tsinghua University, Beijing, China. · Department of Ophthalmology, Ramathibodi Hospital, Mahidol University, Thailand · Suraj Eye Institute, Nagpur, India · Department of Ophthalmology, Seoul National University Hospital, Soeul National University College of Medicine, Seoul, South Korea · USC Roski Eye Institute, Keck School of Medicine, United States of America · Centre for Vision Research, Westmead Institute for Medical Research, The University of Sydney, Sydney, New South Wales, Australia · Faculty of Medicine Ramathibodi Hospital and Faculty of Engineering, Mahidol University, Thailand · SNEC Ocular Reading Centre, Singapore National Eye Centre, Singapore · School of Artificial Intelligence, Sichuan University, Chengdu, China · School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Abstract
Background: Artificial intelligence (AI)-based glaucoma detection from colour fundus photographs (CFP) offers scalable screening, but performance may decline on external datasets because of differences in ground-truth definitions, populations, and coexisting conditions such as high myopia (HM). We developed and validated a Vision Transformer-based deep learning (DL) model for glaucoma detection across multi-ethnic cohorts with and without HM. Methods: A ViT-B/16 model with predictive uncertainty estimation was developed using 56,483 CFPs (57.1% with myopia; 14.4% with HM). Glaucoma labels were standardised using clinical, imaging, and perimetry data. The model was validated on 16 independent datasets across three continents, including four datasets with explicit HM labels. Findings: Internal AUROC was 98.7% (95% CI 98.2-99.1%), with sensitivity 94.5% and specificity 97.3%. Across 16 external datasets from eight countries, AUROCs ranged from 86.4% to 99.6%. In HM eyes, internal AUROC was 97.8% (95% CI 96.1-99.2%), with sensitivity 94.8% and specificity 93.7%. External HM AUROCs were 86.5% in the Beijing Eye Study and 93.3%, 91.8%, and 85.5% in hospital-based datasets from Taiwan, Thailand, and South Korea. In an exploratory HM clinical evaluation, the model had higher CFP-only diagnostic accuracy than ophthalmologists and trained graders (92.0% vs 70.0%; p=0.008) and performed comparably to glaucoma specialists using full clinical information. Interpretation: The model showed robust glaucoma detection across myopic and non-myopic multi-ethnic populations and may support AI-assisted screening in settings with high HM prevalence.