Abstract Backgrounds &amp; Objective Glaucomatous vision loss may be preceded by an enlargement of the cup-to-disc ratio (CDR). We propose to develop and validate an artificial intelligence based CDR grading system that may aid in effective glaucoma-suspect screening. Design, Setting &amp; Participants 1546 disc-centered fundus images were selected including all 457 images from the Retinal Image Database for Optic Nerve Evaluation dataset, and images randomly selected from the Age-Related EyeDisease Study, and Singapore Malay Eye Study to develop the system. First, a proprietary semi-automated software was used by an expert grader to quantify vertical CDR. Then, using CDR below 0.5 (not suspect) and CDR above 0.5 (glaucoma-suspect), deep learning architectures were used to train and test a binary classifier system. Measurements The binary classifier, with glaucoma-suspect as positive, is measured using sensitivity, specificity, accuracy, and AUC. Results The system achieved an accuracy of 89.67% (sensitivity, 83.33%; specificity, 93.89%; AUC, 0.93). For external validation, the Retinal Fundus Image database for Glaucoma Analysis dataset, which has 638 gradable quality images, was used. Here the model achieved an accuracy of 83.54% (sensitivity, 80.11%; specificity, 84.96%; AUC, 0.85). Conclusions Having demonstrated an accurate and fully automated glaucoma-suspect screening system that can be deployed on telemedicine platforms, we plan prospective trials to determine the feasibility of the system in primary care settings.

An Artificial Intelligence and Telemedicine Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging