NCT07471971

Assessment of Hypertensive Retinopathy Using Convolutional Neural Network "RetinAIcheck"

Assessment of Hypertensive Retinopathy Using Keith Wagener Barker's Classification, Based on Convolutional Neural Network "RetinAIcheck"

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 729 (actual)
• Sponsor: I.M. Sechenov First Moscow State Medical University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

The current study is aimed at estimating the diagnostic effectiveness of a developed convolutional neural network (CNN) "RetinAIcheck" in grading the severity of hypertensive retinopathy in patients of the Russian population. The training data set was obtained from an open source and relabeled by seven independent retina specialists, the sample size was 30,000 fundus photographs. The test sample included 729 patients (1401 eyes) with HR. The reference standard was the result of independent grading of HR stage by two ophthalmologists, controversial clinical cases were evaluated with the involvement of a third ophthalmologist.

Conditions

• Hypertensive Retinopathy

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Convolutional neural network "RetinAIcheck"	A convolutional neural network is a medical decision support system that processes digital fundus photographs obtained during mydriasis and determines the probability of the presence/absence of hypertensive retinopathy and it's grading due to Keith Wagener Barker's classification.

Timeline

• Start date: 2021-03-11
• Primary completion: 2026-02-26
• Completion: 2026-02-26
• First posted: 2026-03-13
• Last updated: 2026-03-16

Locations

1 site across 1 country: Russia