Glaucoma patient screening from online retinal fundus images via Artificial Intelligence

ObjectivesTo design and evaluate a novel automated glaucoma classifier from retinal fundus images. MethodsWe designed a novel Artificial Intelligence (AI) automated tool to detect glaucoma from retinal fundus images. We then downloaded publicly available retinal fundus image datasets containing healthy patients and images with verified glaucoma labels. Two thirds of the images were used to train the classifier. The remaining third of the images was used to create several cross-validation evaluation sets with a realistic glaucoma prevalence, to evaluate the classifiers performance in a screening scenario. Results10,658 retinal fundus images from seven different sources were found and downloaded. They were randomly divided into 7,106 for training and 3,551 for validation. Glaucoma prevalence was 24%. Using the validation set, we created 50 random sets of 1,000 images with a 5% glaucoma prevalence. On these sets, the classifier reached a detection rate of 84.1% (CI 95=+-1.1%) and 95.8% (CI 95=+-0.2%) specificity. ConclusionsA novel glaucoma classifier from retinal fundus images showed promising results as screening tool on a large cohort of patients. A large clinical study is needed to confirm these results.