An interventional study of VUNO Med-Fundus AI in Diabetic Retinopathy, Age Related Macular Degeneration and Retinal Vein Occlusion, sponsored by Inje University. Completed at 5 sites in South Korea. Per ClinicalTrials.gov, last updated 2026-06-23.
Sponsored by Inje University · Not applicable, Interventional, and Diagnostic
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.
778 studies on the registry are indexed under Diabetic Retinopathy; 142 are open to participants now.
This study's enrollment of 10 is below the median of 80 across 481 interventional studies indexed under Diabetic Retinopathy.
Browse Diabetic Retinopathy studies →Inje University is the lead sponsor of 93 studies on the registry; 1 is open to participants now.
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Ten readers will be recruited from five participating hospital sites, consisting of:
Ophthalmologists and residents of any age, sex, race, or ethnicity may participate as study readers. All readers must meet the following inclusion criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Readers interpret the fundus images with AI-generated outputs available.
Device: VUNO Med-Fundus AI
Readers interpret fundus images without access to the AI system.
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).
Performance of readers with and without AI assistance: Sensitivity
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.
Time frame: Through study completion, approximately 2 months
Performance of readers with and without AI assistance: Specificity
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.
Time frame: Through study completion, approximately 2 months
Reading time per image
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.
Time frame: Through study completion, approximately 2 months
Plan to share: No
No publications or documents are linked to this record.
This study is completed, as verified in Jan 2026. You cannot join it, but the record below documents what was studied.
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Inje University