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

A Multicenter Clinical Study to Validate the Performance Improvement of Fundus Photography Reading Software

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.

Ten readers will be recruited from five participating hospital sites, consisting of:

• Five ophthalmologists within three years of board certification
• Five ophthalmology residents

Ophthalmologists and residents of any age, sex, race, or ethnicity may participate as study readers. All readers must meet the following inclusion criteria:

• Licensed physicians qualified to interpret fundus images.
• Ophthalmologists within three years of board certification, or ophthalmology residents with no restriction on clinical experience.
• Able and willing to complete both the unassisted and AI-assisted reading sessions.
• Able to provide informed consent for participation in the reader study.
• Affiliated with one of the participating clinical sites.