Assessment of Hypertensive Retinopathy Using Convolutional Neural Network "RetinAIcheck"

Assessment of Hypertensive Retinopathy Using Keith Wagener Barker's Classification, Based on 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.

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.