Hypertensive RetinopathyNCT07471971
Assessment of Hypertensive Retinopathy Using Neural Network "RetinAIcheck"
An observational study of Hypertensive Retinopathy, run by I.M. Sechenov First Moscow State Medical University at 1 site in Russia.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Hypertensive Retinopathy
- What is being tested?
- Convolutional neural network "RetinAIcheck"
- How many people
- 755 participants
- Where
- 1 siteRussia
- Study dates
- 11 Mar 2021to 26 Feb 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The test sample was collected at the Cardiology Clinic of the Sechenov University Clinical Hospital № 1, at the Research Institute of Eye Diseases named after M.M. Krasnov and in the Moscow Regional Clinical Research Institute named after M.F. Vladimirsky (MONIKI).
Who may be able to join
- Patients with and without a diagnosis of arterial hypertension, based on medical records
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Assessment of Hypertensive Retinopathy Using Keith Wagener Barker's Classification, Based on 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.
- Conditions
- Hypertensive Retinopathy
- Being tested
- Convolutional neural network "RetinAIcheck"
- Keywords
- convolutional neural network, hypertensive retinopathy
- Medical subject headings
- Hypertensive Retinopathy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
| Study group | Role | What they receive |
|---|---|---|
| Class 1 hypertensive retinopathy | Not described Receives: Diagnostic Test: Convolutional neural network "RetinAIcheck" | |
| Class 2 hypertensive retinopathy | Not described Receives: Diagnostic Test: Convolutional neural network "RetinAIcheck" | |
| Class 3 hypertensive retinopathy | Not described Receives: Diagnostic Test: Convolutional neural network "RetinAIcheck" | |
| Class 3+4 hypertensive retinopathy | Not described Receives: Diagnostic Test: Convolutional neural network "RetinAIcheck" | |
| Class 0 without signs of hypertensive retinopathy | Not described Receives: Diagnostic Test: Convolutional neural network "RetinAIcheck" |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy
The ability of a test to correctly identify the proportion of true positive cases
Measured: The ability to correctly identify the presence or absence of condition
Secondary outcomes (6)
Sensitivity
The ability of a test to correctly identify the proportion of true positive cases
Measured: February 2026
Specificity
The ability of a test to correctly identify the proportion of true negative cases
Measured: February 2026
Positive predictive value
The probability that a person who tests positive for the condition actually has that one
Measured: February 2026
Negative predictive value
The probability that a person who tests negative for the condition truly does not have it
Measured: February 2026
AUROC, area under the ROC curve (one-versus-rest)
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
Measured: February 2026
Quadratically weighted kappa
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
Measured: February 2026
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Mar 2026
- Study start11 Mar 2021
- Primary completion26 Feb 2026
- Study completion26 Feb 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Hypertensive RetinopathyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 755 participants (actual)
Sponsor and investigators
- Lead sponsor
- I.M. Sechenov First Moscow State Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Philipp Yu Kopylov, Prof., Principal investigator, Sechenov First Moscow State Medical University (Sechenov University)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07471971
- Sponsor's protocol ID
- RetinAIcheck-01
- Status last verified
- Sep 2021
- First submitted
- 10 Mar 2026
- Last updated
- 27 Aug 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Data used in the research project is not openly available, but can be provided upon request to the principal investigator.
Study at a glance
From the registry record, last updated by the sponsor on 27 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Assessment of Hypertensive Retinopathy Using Keith Wagener Barker's Classification, Based on Neural Network "RetinAIcheck"
- ClinicalTrials.gov ID
- NCT07471971
- Acronym / sponsor ID
- RetinAIcheck-01
- Conditions
- Hypertensive Retinopathy
- Keywords
- convolutional neural network, hypertensive retinopathy
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 755 participants (actual)
- Lead sponsor
- I.M. Sechenov First Moscow State Medical UniversityUniversity, hospital or other
- Study start
- 11 Mar 2021 (actual)
- Primary completion
- 26 Feb 2026 (actual)
- Study completion
- 26 Feb 2026 (actual)
- First posted
- 13 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| Class 1 hypertensive retinopathy | – | Diagnostic Test: Convolutional neural network "RetinAIcheck" |
| Class 2 hypertensive retinopathy | – | Diagnostic Test: Convolutional neural network "RetinAIcheck" |
| Class 3 hypertensive retinopathy | – | Diagnostic Test: Convolutional neural network "RetinAIcheck" |
| Class 3+4 hypertensive retinopathy | – | Diagnostic Test: Convolutional neural network "RetinAIcheck" |
| Class 0 without signs of hypertensive retinopathy | – | Diagnostic Test: Convolutional neural network "RetinAIcheck" |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The test sample was collected at the Cardiology Clinic of the Sechenov University Clinical Hospital № 1, at the Research Institute of Eye Diseases named after M.M. Krasnov and in the Moscow Regional Clinical Research Institute named after M.F. Vladimirsky (MONIKI).
Key inclusion criteria (1 listed)
- Patients with and without a diagnosis of arterial hypertension, based on medical records
Key exclusion criteria (7 listed)
- anophthalmia,
- optic nerve atrophy,
- eyeball injuries,
- age-related macular degeneration,
- central serous chorioretinopathy,
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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. | Diagnostic test | Class 0 without signs of hypertensive retinopathy, Class 1 hypertensive retinopathy, Class 2 hypertensive retinopathy, Class 3 hypertensive retinopathy, Class 3+4 hypertensive retinopathy |
Primary outcome
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
6 secondary outcomes
Sensitivity
Time frame: February 2026
Specificity
Time frame: February 2026
Positive predictive value
Time frame: February 2026
Negative predictive value
Time frame: February 2026
AUROC, area under the ROC curve (one-versus-rest)
Time frame: February 2026
Quadratically weighted kappa
Time frame: February 2026
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Aug 2026.
- Related InfoLink provided by the sponsor.
- Individual participant dataSharing planned: No. Data used in the research project is not openly available, but can be provided upon request to the principal investigator.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Philipp Yu Kopylov, Prof. principal investigator
Sechenov First Moscow State Medical University (Sechenov University)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hypertensive Retinopathy, run by I.M. Sechenov First Moscow State Medical University at 1 site in Russia.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 27 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Russia. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy (time frame: The ability to correctly identify the presence or absence of condition). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Aug 2026.