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Completed

NCT Number: NCT07471971

Assessment of Hypertensive Retinopathy Using Convolutional Neural Network "RetinAIcheck"

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.

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Key information

Conditions

Hypertensive Retinopathy Cardiovascular Diseases Eye Diseases Hypertension Retinal Diseases Vascular Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

University Clinical Hospital №1, Sechenov University

Moscow, Russia

Who can participate

Healthy volunteers accepted: Yes

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

Inclusion criteria

  • The presence of a diagnosis of hypertension in the patient's electronic medical record.

Exclusion criteria

  • anophthalmia,
  • optic nerve atrophy,
  • eyeball injuries,
  • age-related macular degeneration,
  • central serous chorioretinopathy,
  • central serous chorioretinitis,
  • clouding of the optical media of the eye, which affects the quality of the image.

Treatment and study plan

Convolutional neural network "RetinAIcheck"

Diagnostic Test

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.

Primary outcomes

  1. Accuracy

    Time frame: The ability to correctly identify the presence or absence of condition

    The ability of a test to correctly identify the proportion of true positive cases

Secondary outcomes

  1. Sensitivity

    Time frame: February 2026

    The ability of a test to correctly identify the proportion of true positive cases

  2. Specificity

    Time frame: February 2026

    The ability of a test to correctly identify the proportion of true negative cases

Sponsors and collaborators

Lead sponsor

I.M. Sechenov First Moscow State Medical University

Other

Registry information

Official study title

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

Important dates

Study start
2021
Primary completion
2026
Study completion
2026
First posted
Mar 13, 2026
Registry last updated
Mar 16, 2026

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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