AI-Assisted Detection of Posterior Segment Diseases: DR, AMD, RVO, and Glaucoma

The purpose of this multi-center study is to evaluate the extent to which AI-assisted fundus image interpretation improves the diagnostic performance of ophthalmologists. Rather than assessing the standalone algorithm performance, this study aims to determine the clinical value of using AI as a decision-support tool within actual clinical workflows. At each participating institution, five ophthalmologists within three years of board certification and five ophthalmology residents will participate as readers. All readers will interpret fundus images both with and without the AI-based assistance software. The study will quantitatively compare diagnostic accuracy and reading time across the two conditions for four posterior segment diseases: diabetic retinopathy, age-related macular degeneration, retinal vein occlusion, and glaucoma.

NCT Number	NCT07318428
Lead Sponsor	Inje University Collaborators: Dong-A University Hospital, Kosin University Gospel Hospital, Pusan National University Hospital, Pusan National University Yangsan Hospital
Conditions	Diabetic Retinopathy, Age Related Macular Degeneration, Retinal Vein Occlusion, Glaucoma, Glaucoma Suspect
Enrollment	10 participants
Start Date	2026-02-20
Primary Completion	2026-04-03 (estimated)
Study Completion	2026-05-22 (estimated)
Updated on ClinicalTrials.gov	2026-06-23