Assessment of Hypertensive Retinopathy Using Convolutional Neural Network "RetinAIcheck"

N/ACompletedNCT07471971

I.M. Sechenov First Moscow State Medical University729 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

729

Conditions

Hypertensive Retinopathy

Interventions

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.

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

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.

Outcomes

Primary Outcomes

Accuracy

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

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

Secondary Outcomes

Sensitivity