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Assessment of Hypertensive Retinopathy Using Neural Network "RetinAIcheck"
Sponsor: I.M. Sechenov First Moscow State Medical University
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.
Official title: Assessment of Hypertensive Retinopathy Using Keith Wagener Barker's Classification, Based on Neural Network "RetinAIcheck"
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
755
Start Date
2021-03-11
Completion Date
2026-02-26
Last Updated
2026-08-27
Healthy Volunteers
Yes
Conditions
Interventions
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.
Locations (1)
University Clinical Hospital №1, Sechenov University
Moscow, Russia