Development of deep learning model to screen for primary open-angle glaucoma in African ancestry individuals

Primary open-angle glaucoma (POAG) screening using artificial intelligence (AI) has emerged as a transformative method to identify undiagnosed disease. African ancestry individuals are under-represented in current datasets for AI models, despite being disproportionally affected by this blinding disease. We developed a deep learning model that screens for POAG using fundus photography from Primary Open-Angle African American Glaucoma Genetics (POAAGG) subjects (n = 64,129 images, including 42,914 images from 1782 cases and 21,215 images from 682 controls). Our final diagnosis pipeline is as follows: (1) select the six most informative images from single timepoint using a Binary Classifier, (2) predict POAG probability from each image using Vision-Transformer, (3) make final POAG predictions by averaging predicted probabilities across selected images (AUC = 0.925). The model was evaluated on the REFUGE-1 dataset of Chinese ancestry individuals (AUC = 0.920). Our model has applications to POAG screening in public settings such as primary care offices, as well as low-resource settings.