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

Diabetic Retinopathy Clinical Trial

— AIMDR  

Official title:

USING ARTIFICIAL INTELLIGENCE FOR MASS SCREENING OF THE DIABETIC RETINOPATHY

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06112691
• Other study ID #: 24.01.2022
• Status: Recruiting
• Start date: February 1, 2022
• Est. completion date: November 2024

Study information

• Verified date: June 2023
• Source: The Filatov Institute of Eye Diseases and Tissue Therapy
• Contact: Andrii MD Korol, PhD
• Phone: 380936327266
• Email: andrii.r.korol[@]gmail.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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

Description:

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).

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 660
• Est. completion date: November 2024
• Est. primary completion date: October 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 90 Years

Eligibility

Inclusion Criteria:

1. 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).

2. Understanding of the Study and willingness and ability to sign informed consent

3. Patient age 18 or above

4. Diagnostic for diabetes: 4a) Type 1 diabetes of a lest 5 years of evolution; or 4b) Type 2 diabetes

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Exclusion Criteria:

1. Patients under 18 years of age;

2. Failure to give informed consent;

3. 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.

4. 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.

Related Conditions & MeSH terms

• Diabetic Retinopathy
• Retinal Diseases

Intervention

Device:
• taking fundus photos using non-mydriatic fundus camera - FundusScope Rodenstock
using artificial intelligence to identify diabetic retinopathy in the early stages using fundus photography.

Locations

Country	Name	City	State
Ukraine	The Filatov Institute of Eye Diseases and Tissue Therapy	Odessa

Sponsors (3)

Lead Sponsor	Collaborator
The Filatov Institute of Eye Diseases and Tissue Therapy	CheckEye LLC, Komisarenko Institute of Endocrinology and Metobolism

Country where clinical trial is conducted

Ukraine, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The accuracy	The accuracy of detecting of DR	Baseline-Month 12
Secondary	The percent of invalid images	The percent of invalid images for analysing by neural network	Baseline-Month 12
Secondary	The percent of false positive detection of DR	The percent of false positive detection of DR in individuals without DR	Baseline-Month 12