ARTIFICIAL INTELLIGENCE FOR MASS SCREENING OF THE DIABETIC RETINOPATHY IN THE CHERNIVTSI REGION (Pilot Study)

Study Overview
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Brief Summary
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The purpose of this study is to create a patient-centric environment for early detection of DR with AI-driven solutions.

Detailed Description
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The purpose of this study is to create a patient-centric environment for early detection of DR with AI-driven solutions. This study is planned as a follow-up. Participants who meet the eligibility criteria will be recruited from sites staffed by the trained photographers. After assessing eligibility and securing written informed consent, fundus photographs will be captured using a nonmydriatic ocular fundus camera. Images will be taken according to a specific RAssbyAI Check Eye's imaging protocol provided to camera operator, and then analyzed by the RAssbyAI Check Eye's. The photography protocol consists of two images of the ocular fundus (one optic disc centered, one fovea centered).

Official Title
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USING ARTIFICIAL INTELLIGENCE FOR MASS SCREENING OF THE DIABETIC RETINOPATHY

Conditions
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Diabetic Retinopathy

Intervention / Treatment
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* Device: taking fundus photos using non-mydriatic fundus camera - FundusScope Rodenstock


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Documented diagnosis of Diabetes mellitus as defined by: A. Having met the criteria established by either the World Health Organization (WHO) or the American Diabetes Association (ADA) B. Hemoglobin A1c (HbA1c)>= 6.5% based on repeated assessments C. Fasting Plasma Glucose (FPG) >= 126 mg/dL (7.0 mmol/L) based on repeated assessments D. Oral Glucose Tolerance test with 2 hr plasma glucose >= 200 mg/dL (11.1mmol/L) using equivalent of 75g anhydrous glucose dose in water. E. Symptoms of hyperglycemia or hyperglycemic crisis with random plasma glucose >=200mg/dL (11.1 mmol/L). Understanding of the Study and willingness and ability to sign informed consent Patient age 18 or above Diagnostic for diabetes: 4a) Type 1 diabetes of a lest 5 years of evolution; or 4b) Type 2 diabetes - Exclusion Criteria: Patients under 18 years of age; Failure to give informed consent; Presence of retinal diseases - acquired disease: age-related macular degeneration (AMD), occlusion of retinal vessels (ORV), etc.; birth defects: coloboma of choroid or optic nerve disc, etc.; hereditary diseases: retinitis pigmentosa, angioid streaks of the retina, etc. A patient who has already undergone treatment (surgery, laser, etc.) for any disease of the retina: age-related macular degeneration (AMD), retinal vascular occlusion (ARV), etc. These patients should be excluded or allocated to a separate group.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 90 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| main group<br>diabetes mellitus | Device: taking fundus photos using non-mydriatic fundus camera - FundusScope Rodenstock<br>* using artificial intelligence to identify diabetic retinopathy in the early stages using fundus photography.<br>|
| control group<br>without diagnosis of diabetes mellitus without retinal diseases | Device: taking fundus photos using non-mydriatic fundus camera - FundusScope Rodenstock<br>* using artificial intelligence to identify diabetic retinopathy in the early stages using fundus photography.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The accuracy | The accuracy of detecting of DR | Baseline-Month 12 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The percent of invalid images | The percent of invalid images for analysing by neural network | Baseline-Month 12 | 
| The percent of false positive detection of DR | The percent of false positive detection of DR in individuals without DR | Baseline-Month 12 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Fundus photo, Artificail Intelligence, Diabetes Melitus, Screening, CheckEye, Fundus image, Diabetic Retinopathy, nonmydriatic camera