Abstract
This article is devoted to the development of an algorithm for segmentation of visual signs of cystoid macular edema (including diabetic macular edema), age-related macular degeneration (choroidal neovascularization and retinal drusen), central serous choroidopathy and epiretinal membrane on optical coherence tomography (OCT) images. The paper presents the world statistics of patients with these pathologies, and their needs for regular ophthalmological screening. As a solution to the problem of regular screening, the use of telemedicine applications has been proposed. With the help of artificial intelligence, the main visual signs of these pathologies are determined, which are detected on digital OCT images of the retina. A list of scientific and technical problems that needed to be solved is presented: the collection of a training database, data markup and the choice of artificial neural network architectures for feature segmentation problems. The algorithm validation process is described and the current results are presented.
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