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Completed

NCT Number: NCT07471971

Assessment of Hypertensive Retinopathy Using Neural Network "RetinAIcheck"

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.

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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

  • Patients with and without a diagnosis of arterial hypertension, based on medical records

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

  3. Positive predictive value

    Time frame: February 2026

    The probability that a person who tests positive for the condition actually has that one

  4. Negative predictive value

    Time frame: February 2026

    The probability that a person who tests negative for the condition truly does not have it

  5. AUROC, area under the ROC curve (one-versus-rest)

    Time frame: February 2026

    An average metric used to evaluate multi-class classification models by computing the Area Under the ROC Curve for each class separately against all other classes and then averaging the results

  6. Quadratically weighted kappa

    Time frame: February 2026

    A statistical measure that evaluates the level of agreement between two raters or outcomes on an ordinal scale, penalizing errors based on the squared distance between categories

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 Neural Network "RetinAIcheck"

Important dates

Study start
2021
Primary completion
2026
Study completion
2026
First posted
Mar 13, 2026
Registry last updated
Aug 27, 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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