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NCT Number: NCT07378956

Clinical Efficacy of Implementing an AI-SaMD for Funduscopy Analysis in Patients With Diabetes Mellitus

The objective of this study is to investigate the efficacy of implementing the AI-SaMD(VUNO Med®-Fundus AI™) alongside routine clinical practice for the detection of diabetic retinopathy.

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Key information

Conditions

Diabetic Retinopathy (DR) Cardiovascular Diseases Diabete Mellitus Diabetes Complications Diabetes Mellitus Diabetic Angiopathies Diabetic Retinopathy Endocrine System Diseases Eye Diseases Fundus Photography Glucose Metabolism Disorders Metabolic Diseases Nutritional and Metabolic Diseases Retinal Diseases Vascular Diseases

Age range

19 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Inha University Hospital

Incheon, Gyeonggi-do, 22332, South Korea

Location status: Recruiting

About this study

The primary objective of this study is to compare the true referral rate between patients with VUNO Med®-Fundus AI™-assisted screening (intervention group) and those receiving usual clinical care without AI assistance (control group) among patients with diabetes mellitus.

Who can participate

Healthy volunteers accepted: No

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

Inclusion criteria

  • Adults aged 19 years or older.
  • A documented diagnosis of type 2 diabetes mellitus.
  • Ability to communicate adequately and provide written informed consent for participation in the study.

Exclusion criteria

  • A prior diagnosis of diabetic retinopathy at the time of screening.
  • A history of ophthalmic surgery within 6 months prior to the screening date.
  • A diagnosis of type 1 diabetes mellitus.
  • Pregnancy at the time of screening.
  • Any condition that, in the opinion of the investigator, would make participation in the study infeasible or inappropriate.

Treatment and study plan

VUNO Med®-Fundus AI™

Device

VUNO Med®-Fundus AI™ is an artificial intelligence-based fundus image detection and diagnostic support software. The software automatically identifies abnormal retinal findings and provides information on the type and location of detected abnormalities to aid clinical decision-making.

Primary outcomes

  1. True Referral Rate

    Time frame: within 6 months

    The true referral rate is defined as the proportion of subjects who were diagnosed with diabetic retinopathy by an ophthalmologist among those referred to ophthalmology with suspected diabetic retinopathy. The true referral rate will be compared between the intervention and control groups.

Secondary outcomes

  1. Diabetic Retinopathy (DR) Diagnosis Rate

    Time frame: Within 6 months

    Proportion of subjects diagnosed with diabetic retinopathy among all enrolled subjects will be compared between the intervention group and the control group.

  2. Odds Ratio

    Time frame: Within 6 months

    Odds ratio between the diagnosis of diabetic retinopathy and the application of the AI system among subjects referred to ophthalmology will be calculated.

  3. Referral Rate

    Time frame: Within 6 months

    Proportion of subjects referred to ophthalmology for suspected diabetic retinopathy among all enrolled subjects will be compared between the intervention group and the control group.

  4. Time to Diabetic Retinopathy Diagnosis

    Time frame: Within 6 months

    Time interval from the diagnosis of diabetes mellitus to confirmed diagnosis of diabetic retinopathy based on ophthalmologic evaluation among referred subjects will be compared between the intervention group and the control group.

  5. Performance of the AI System in Detecting Diabetic Retinopathy

    Time frame: Within 6 months

    Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of the AI system will be calculated in comparison with ophthalmologist-confirmed diagnoses.

  6. Accuracy of Referral for Diabetic Retinopathy

    Time frame: Within 6 months

    Proportion of subjects whose referral decisions (referral or non-referral) are concordant with ophthalmologist-confirmed DR status will be compared between the intervention group and the control group.

  7. Adherence Rate

    Time frame: Within 6 months

    Proportion of referred subjects who attend an ophthalmology department will be compared between the intervention group and the control group.

Other outcomes

  1. Interim Analysis

    Time frame: Within 6 months

    One interim analysis will be conducted when 50% of subjects have completed all study visits. The purpose of this analysis is to review the status of subject dropouts and the reasons for dropout in each group. This procedure will be conducted by a statistician who is independent of the study conduct.

Study contacts

Contact information is provided by the study sponsor or research team.

Hee Jun Park

CONTACT

[email protected]

82-10-7101-2844

Sponsors and collaborators

Lead sponsor

VUNO Inc.

Industry

Registry information

Official study title

Clinical Efficacy of Implementing an AI-Driven Software as a Medical Device (SaMD) for Funduscopy Analysis in Patients With Diabetes Mellitus: A Randomized Controlled Trial Protocol

Acronym: SAFE-DM

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Jan 30, 2026
Registry last updated
Apr 13, 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.

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