Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy

Question How does a deep learning system compare with professional human graders in detecting glaucomatous optic neuropathy? Findings In this cross-sectional study, the deep learning system showed a sensitivity and specificity of greater than 90% for detecting glaucomatous optic neuropathy in a local validation dataset, in 3 clinical-based datasets, and in a real-world distribution dataset. The deep learning system showed lower sensitivity when tested in multiethnic and website-based datasets. Meaning This assessment of fundus images suggests that deep learning systems can provide a tool with high sensitivity and specificity that might expedite screening for glaucomatous optic neuropathy. Importance A deep learning system (DLS) that could automatically detect glaucomatous optic neuropathy (GON) with high sensitivity and specificity could expedite screening for GON. Objective To establish a DLS for detection of GON using retinal fundus images and glaucoma diagnosis with convoluted neural networks (GD-CNN) that has the ability to be generalized across populations. Design, Setting, and Participants In this cross-sectional study, a DLS for the classification of GON was developed for automated classification of GON using retinal fundus images obtained from the Chinese Glaucoma Study Alliance, the Handan Eye Study, and online databases.