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

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06879834

Acronym

ARTDR

Enrollment

200

Registered

2025-03-17

Start date

2024-11-02

Completion date

2025-12-31

Last updated

2025-03-17

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Diabetic Retinopathy

Keywords

Diabetic Retinopathy, Fundus photo, nonmydriatic camera, Artificail Intelligence, CheckEye, Fundus image, fundus photography

Brief summary

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

Detailed description

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.

Interventions

DEVICEtaking fundus photos using non-mydriatic fundus camera

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

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 90 Years

Healthy volunteers

Yes

Inclusion criteria

1. Documented diagnosis of diabetes mellitus by definition. 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

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.

Design outcomes

Primary

Measure	Time frame	Description
The accuracy	Baseline	The accuracy of detecting of DR

Secondary

Measure	Time frame	Description
The percent of invalid images	Baseline	The percent of invalid images for analysing by neural network
The percent of false positive detection of DR	Baseline	The percent of false positive detection of DR in individuals without DR

Countries

Ukraine

Contacts

Primary ContactAndrii MD Korol, PhD

andrii.r.korol@gmail.com380936327266

Backup ContactOlha MD Pohosian

olha.a.pohosian@gmail.com380932084927

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026