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Assessment of Hypertensive Retinopathy Using Neural Network "RetinAIcheck"

NCT07471971 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 755

Last updated 2026-08-27

No results posted yet for this study

Summary

The current study is aimed at estimating the diagnostic effectiveness of a developed neural network "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 755 patients (1374 eyes). Among the 1.374 eyes, 94 were without HR (class 0), 330 had class 1, 660 had class 2, 280 had class 3, and 10 had class 4 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

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.

Sponsors & Collaborators

  • I.M. Sechenov First Moscow State Medical University

    lead OTHER

Principal Investigators

  • Philipp Yu Kopylov, Prof. · Sechenov First Moscow State Medical University (Sechenov University)

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2021-03-11
Primary Completion
2026-02-26
Completion
2026-02-26

Countries

  • Russia

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07471971 on ClinicalTrials.gov