Clinical Efficacy of Implementing an AI-SaMD for Funduscopy Analysis in Patients With Diabetes Mellitus
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: VUNO Med®-Fundus AI™.
- Who it may be relevant to
- Registry conditions: Diabetic Retinopathy (DR), Diabete Mellitus, Fundus Photography. Basic parameters: from 19 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- South Korea
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official 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
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.
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
- Device VUNO 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.
Primary outcome measures
- True Referral Rate [Time frame: within 6 months]
Secondary outcome measures (7)
- Diabetic Retinopathy (DR) Diagnosis Rate [Time frame: Within 6 months]
- Odds Ratio [Time frame: Within 6 months]
- Referral Rate [Time frame: Within 6 months]
- Time to Diabetic Retinopathy Diagnosis [Time frame: Within 6 months]
- Performance of the AI System in Detecting Diabetic Retinopathy [Time frame: Within 6 months]
- Accuracy of Referral for Diabetic Retinopathy [Time frame: Within 6 months]
- Adherence Rate [Time frame: Within 6 months]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Open label
- Primary purpose
- Screening
Study locations
South Korea · 1 center
- Inha University Hospital — Incheon
Identifiers
NCT: NCT07378956 · VN-M-03 PR