AI-Assisted Detection of Posterior Segment Diseases: DR, AMD, RVO, and Glaucoma
NCT ID: NCT07318428
Last Updated: 2026-01-07
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
NA
10 participants
INTERVENTIONAL
2026-02-01
2026-04-30
Brief Summary
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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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Detailed Description
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Conditions
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Study Design
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NON_RANDOMIZED
SEQUENTIAL
DIAGNOSTIC
NONE
Study Groups
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AI-Assisted Reading
Readers interpret the fundus images with AI-generated outputs available.
VUNO Med-Fundus AI
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).
Unassisted Reading
Readers interpret fundus images without access to the AI system.
No interventions assigned to this group
Interventions
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VUNO Med-Fundus AI
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).
Eligibility Criteria
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Inclusion Criteria
* 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.
ALL
No
Sponsors
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Dong-A University Hospital
OTHER
Kosin University Gospel Hospital
OTHER
Pusan National University Hospital
OTHER
Pusan National University Yangsan Hospital
OTHER
Inje University
OTHER
Responsible Party
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Dong Geun Kim
Assistant Professor of Ophthalmology
Locations
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Inje University Busan Paik Hospital
Busan, , South Korea
Dong-A University Hospital
Busan, , South Korea
Pusan National University Hospital
Busan, , South Korea
Kosin University Gospel Hospital
Busan, , South Korea
Pusan National University Yangsan Hospital
Yangsan, , South Korea
Countries
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Other Identifiers
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2025-07-033
Identifier Type: -
Identifier Source: org_study_id
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