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.

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Key information

Conditions

Diabetic Retinopathy Age Related Macular Degeneration Cardiovascular Diseases Diabetes Complications Diabetes Mellitus Diabetic Angiopathies Embolism and Thrombosis Endocrine System Diseases Eye Diseases Glaucoma Glaucoma Suspect Macular Degeneration Ocular Hypertension Retinal Degeneration Retinal Diseases Retinal Vein Occlusion Thrombosis Vascular Diseases Venous Thrombosis

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Dong-A University Hospital, Busan, South Korea

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

VUNO Med-Fundus AI

Device

The intervention consists of an AI-based fundus image interpretation software that provides automated outputs for 12 retinal and optic nerve findings (e.g., hemorrhage, exudates, drusen, optic disc change). The system does not generate a direct disease diagnosis. Instead, the AI displays the presence or absence of 12 predefined findings along with their lesion locations. Readers may use this finding-level information as decision-support when determining the presence of the four target diseases (diabetic retinopathy, age-related macular degeneration, retinal vein occlusion, and glaucoma).

Primary outcomes

Performance of readers with and without AI assistance: Sensitivity

Time frame: Through study completion, approximately 2 months

Sensitivity of reader diagnoses for each of the four target diseases (DR, AMD, RVO, glaucoma) and for any fundus abnormality will be assessed with and without AI assistance, using the image-level reference standard as the comparator, through two reading sessions in which all 10 readers review all cases-randomised for each reader-with a washout period implemented to mitigate recall bias.
Performance of readers with and without AI assistance: Specificity

Time frame: Through study completion, approximately 2 months

Specificity of reader diagnoses for each of the four target diseases (DR, AMD, RVO, glaucoma) and for any fundus abnormality will be assessed with and without AI assistance, using the image-level reference standard as the comparator, through two reading sessions in which all 10 readers review all cases-randomised for each reader-with a washout period implemented to mitigate recall bias.
Reading time per image

Time frame: Through study completion, approximately 2 months

Reading time per image will be measured during both unassisted and AI-assisted interpretation sessions. For each case, the total time from the moment the image is displayed to the moment the reader submits the final disease classification will be recorded automatically by the reading platform. Mean reading time per image will be calculated for each reader and compared between the two conditions to evaluate whether AI assistance reduces interpretation time.

Sponsors and collaborators

Lead sponsor

Inje University

Other

Collaborators

Dong-A University Hospital
Kosin University Gospel Hospital
Pusan National University Hospital
Pusan National University Yangsan Hospital

Registry information

Official study title

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

Important dates

Study start: 2026
Primary completion: 2026
Study completion: 2026
First posted: Jan 6, 2026
Registry last updated: Jun 23, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed