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

VUNO Inc.340 enrolled

Overview

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.

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.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

SCREENING

Masking

NONE

Enrollment

340

Conditions

Diabetic Retinopathy (DR)Diabete Mellitus Fundus Photography

Interventions

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.

Eligibility

Sex: ALLMin age: 19 Years

Medical Language ↔ Plain English

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.

Locations (1)

Inha University Hospital

Incheon, Gyeonggi-do, South Korea

RECRUITING

Outcomes

Primary Outcomes

True Referral Rate

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.

Time frame: within 6 months

Secondary Outcomes

Diabetic Retinopathy (DR) Diagnosis Rate

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

Time frame: Within 6 months

Odds Ratio

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

Time frame: Within 6 months

Referral Rate

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.

Time frame: Within 6 months

Time to Diabetic Retinopathy Diagnosis

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.

Time frame: Within 6 months

Performance of the AI System in Detecting Diabetic Retinopathy

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.

Central Contacts

Hee Jun Park

CONTACT

82-10-7101-2844 heejun.park@vuno.co

Data from ClinicalTrials.gov

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