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

Abstract

Backgrounds & ObjectiveGlaucomatous 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 & Participants1546 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. MeasurementsThe binary classifier, with glaucoma-suspect as positive, is measured using sensitivity, specificity, accuracy, and AUC. ResultsThe 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). ConclusionsHaving 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.