Glaucoma Detection and Feature Identification via GPT-4V Fundus Image Analysis
Issued Date
2025-03-01
Resource Type
eISSN
26669145
Scopus ID
2-s2.0-85214503050
Journal Title
Ophthalmology Science
Volume
5
Issue
2
Rights Holder(s)
SCOPUS
Bibliographic Citation
Ophthalmology Science Vol.5 No.2 (2025)
Suggested Citation
Jalili J., Jiravarnsirikul A., Bowd C., Chuter B., Belghith A., Goldbaum M.H., Baxter S.L., Weinreb R.N., Zangwill L.M., Christopher M. Glaucoma Detection and Feature Identification via GPT-4V Fundus Image Analysis. Ophthalmology Science Vol.5 No.2 (2025). doi:10.1016/j.xops.2024.100667 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/102817
Title
Glaucoma Detection and Feature Identification via GPT-4V Fundus Image Analysis
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
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 of glaucoma. Each image was analyzed twice by GPT-4V to evaluate consistency in its predictions. Two expert graders independently evaluated the same images using identical criteria. Comparisons between GPT-4V's assessments, expert evaluations, and dataset labels were made to determine accuracy, sensitivity, specificity, and Cohen kappa. Main Outcome Measures: The main parameters measured were the accuracy, sensitivity, specificity, and Cohen kappa of GPT-4V in detecting glaucoma compared with expert evaluations. Results: GPT-4V successfully provided glaucoma assessments for all 300 fundus images across the datasets, although approximately 35% required multiple prompt submissions. GPT-4V's overall accuracy in glaucoma detection was slightly lower (0.68, 0.70, and 0.81, respectively) than that of expert graders (0.78, 0.80, and 0.88, for expert grader 1 and 0.72, 0.78, and 0.87, for expert grader 2, respectively), across the ACRIMA, ORIGA, and RIM-ONE datasets. In Glaucoma detection, GPT-4V showed variable agreement by dataset and expert graders, with Cohen kappa values ranging from 0.08 to 0.72. In terms of feature detection, GPT-4V demonstrated high consistency (repeatability) in image gradability, with an agreement accuracy of ≥89% and substantial agreement in rim thinning and cup-to-disc ratio assessments, although kappas were generally lower than expert-to-expert agreement. Conclusions: GPT-4V shows promise as a tool in glaucoma screening and detection through fundus image analysis, demonstrating generally high agreement with expert evaluations of key diagnostic features, although agreement did vary substantially across datasets. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.