Diabete Mellitus, Diabetic Retinopathy (DR), Fundus Photography
Conditions
Brief summary
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.
Detailed description
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.
Interventions
DEVICEVUNO Med®-Fundus AI™
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.
Sponsors
VUNO Inc.
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SCREENING
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
19 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| True Referral Rate | 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
| Measure | Time frame | Description |
|---|---|---|
| Diabetic Retinopathy (DR) Diagnosis Rate | 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. |
| Odds Ratio | 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. |
| Referral Rate | 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. |
| Time to Diabetic Retinopathy Diagnosis | 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. |
| Performance of the AI System in Detecting Diabetic Retinopathy | 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. |
| Accuracy of Referral for Diabetic Retinopathy | 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. |
| Adherence Rate | Within 6 months | Proportion of referred subjects who attend an ophthalmology department will be compared between the intervention group and the control group. |
Countries
South Korea
Contacts
CONTACTHee Jun Park
Outcome results
None listed