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NCT Number: NCT06879834

Assessing of Artificial Intelligence-based Software Platform for Diabetic Retinopathy Screening

To examine the potential for the detection of diabetic retinopathy (DR) using the artificial intelligence (AI)-based software platform Retina-AI.

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Key information

Conditions

Diabetic Retinopathy Cardiovascular Diseases Diabetes Complications Diabetes Mellitus Diabetic Angiopathies Endocrine System Diseases Eye Diseases Retinal Diseases Vascular Diseases

Age range

18 year–90 year

Sex eligibility

All sexes

Study type

Observational

Primary location

The Filatov Institute of Eye Diseases and Tissue Therapy

Odesa, 65061, Ukraine

Location status: Recruiting

About this study

Operator took fundus images with a non-mydriatic fundus camera as per the Retina-AI CheckEye imaging protocol (an optic disc centered image and a fovea centered image for each eye).Thereafter, operator uploaded fundus images in the AI system for processing by the neural network.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Documented diagnosis of diabetes mellitus by definition.
  • 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

-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.

  • 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.

Treatment and study plan

taking fundus photos using non-mydriatic fundus camera

Device

using artificial intelligence to identify diabetic retinopathy in the early stages using fundus photography.

Primary outcomes

  1. The accuracy

    Time frame: Baseline

    The accuracy of detecting of DR

Secondary outcomes

  1. The percent of invalid images

    Time frame: Baseline

    The percent of invalid images for analysing by neural network

  2. The percent of false positive detection of DR

    Time frame: Baseline

    The percent of false positive detection of DR in individuals without DR

Study contacts

Contact information is provided by the study sponsor or research team.

Andrii MD Korol, PhD

CONTACT

[email protected]

380936327266

Olha MD Pohosian

CONTACT

[email protected]

380932084927

Sponsors and collaborators

Lead sponsor

The Filatov Institute of Eye Diseases and Tissue Therapy

Other

Collaborators

  • CheckEye LLC
  • Oftacentro SA

Registry information

Acronym: ARTDR

Important dates

Study start
2024
Primary completion
2025
Study completion
2025
First posted
Mar 17, 2025
Registry last updated
Mar 17, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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