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

Inje University

Status: Not yet enrolling

Conditions: Age Related Macular Degeneration; Glaucoma; Retinal Vein Occlusion; Glaucoma Suspect; Diabetic Retinopathy

Treatments: Device: VUNO Med-Fundus AI

Study type: Interventional

Funder types: Other

Identifiers

NCT07318428

2025-07-033

Details and patient eligibility

About

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.

Enrollment: 10 estimated patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

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.

Trial design

Primary purpose: Diagnostic

Allocation: Non-Randomized

Interventional model: Sequential Assignment

Masking: None (Open label)

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10 participants in 2 patient groups

AI-Assisted Reading

Experimental group

Description:

Readers interpret the fundus images with AI-generated outputs available.

Treatment:

Device: VUNO Med-Fundus AI

Unassisted Reading

No Intervention group

Description:

Readers interpret fundus images without access to the AI system.