Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.

Improving our understanding, and detection, of glaucomatous damage: An approach based upon optical coherence tomography (OCT).

Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects.

24-2 Visual Fields Miss Central Defects Shown on 10-2 Tests in Glaucoma Suspects, Ocular Hypertensives, and Early Glaucoma.

Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective.

Deep learning in ophthalmology: The technical and clinical considerations.

Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs.