Expert-Level Detection of Referable Glaucoma from Fundus Photographs in a Safety Net Population: The AI and Teleophthalmology in Los Angeles Initiative.

Purpose: To develop and test a deep learning (DL) algorithm for detecting referable glaucoma in the Los Angeles County (LAC) Department of Health Services (DHS) teleretinal screening program.

Methods: Fundus photographs and patient-level labels of referable glaucoma (defined as cup-to-disc ratio [CDR] ≥ 0.6) provided by 21 trained optometrist graders were obtained from the LAC DHS teleretinal screening program.

Hemiretinal Asymmetry in Peripapillary Vessel Density in Healthy, Glaucoma Suspect, and Glaucoma Eyes.

Purpose: To investigate hemiretinal asymmetry in radial peripapillary capillary vessel area density (VAD) of healthy, glaucoma suspect, and glaucoma eyes of varying severity and its diagnostic utility for glaucoma.

Design: Population-based, cross-sectional study.

Methods: Optic disc scans (6 × 6 mm) were collected on optical coherence tomography angiography (OCTA) to obtain VAD and on optical coherence tomography (OCT) to measure circumpapillary retinal nerve fiber layer (RNFL) thickness.