Chuter Benton

Chuter Benton, Joshi Vedant, Hallaj Shahin, Walker Evan, Bowd Christopher, Belghith Akram et al.

A novel AI model for glaucoma detection showed multimodal imaging (CFP + OCT) was better than CFP alone, but not significantly better than OCT alone, suggesting OCT-based AI may suffice clinically.

Jalili Jalil, Jiravarnsirikul Anuwat, Bowd Christopher, Chuter Benton, Belghith Akram, Goldbaum Michael H et al.

GPT-4V analyzed fundus images for glaucoma detection and features. It showed promise, with variable agreement to experts, suggesting potential for screening but needing refinement for clinical use.

Chuter Benton, Huynh Justin, Hallaj Shahin, Walker Evan, Liebmann Jeffrey M, Fazio Massimo A et al.

This study found an AI model (RETFound) effectively detects glaucoma from fundus photos, improving with more training data and performing consistently across diverse populations, making it a promising diagnostic tool.

Chuter Benton, Huynh Justin, Bowd Christopher, Walker Evan, Rezapour Jasmin, Brye Nicole et al.

A deep learning model accurately assessed fundus photo quality, and filtering out low-quality images significantly improved automated glaucoma detection, enhancing efficiency and accuracy in clinical screening.

Fan Rui, Bowd Christopher, Christopher Mark, Brye Nicole, Proudfoot James A, Rezapour Jasmin et al.

Deep learning accurately detected glaucoma from fundus photos, sometimes earlier than expert committees, suggesting potential for automated, standardized glaucoma detection in clinical trials and practice.