Glaucoma Detection and Feature Identification via GPT-4V Fundus Image Analysis.

ABSTRACT:

Purpose

The aim is to assess GPT-4V's (OpenAI) diagnostic accuracy and its capability to identify glaucoma-related features compared to expert evaluations.

Design

Evaluation of multimodal large language models for reviewing fundus images in glaucoma.

Subjects

A total of 300 fundus images from 3 public datasets (ACRIMA, ORIGA, and RIM-One v3) that included 139 glaucomatous and 161 nonglaucomatous cases were analyzed.

Methods

Preprocessing ensured each image was centered on the optic disc. GPT-4's vision-preview model (GPT-4V) assessed each image for various glaucoma-related criteria: image quality, image gradability, cup-to-disc ratio, peripapillary atrophy, disc hemorrhages, rim thinning (by quadrant and clock hour), glaucoma status, and estimated probability o

SUBMITTER: Jalili J 

PROVIDER: S-EPMC11773068 | biostudies-literature | 2025 Mar-Apr

REPOSITORIES: biostudies-literature