Automated system for analysis of OCT retina images development and testing

Neovascular age-related macular degeneration ($n$-AMD) is a form of AMD that is responsible for most cases of severe vision loss. Anti-VEGF therapy, which is the gold standard for the treatment of this pathology, is accompanied by OCT monitoring. However, this process is hampered by the lack of methods for accurately quantifying OCT images. The aim of this study is to develope and evaluate the accuracy of the automated calculation of the quantitative characteristics of PED, SRF and IRF biomarkers. A neural network with U-NET architecture was trained on a manually annotated dataset that included 385 OCT images. The dice coefficient measured on the validation dataset was 0.9, 0.72 and 0.69 for PED, SRF and IRF. The results of the quantitative calculation of these biomarkers did not statistically differ from the measurements of an ophthalmologist. Comparison of groups with respect to the anatomical outcome of therapy showed that PED height, extent, and square are different for groups with adherence and non-adherence PED; and PED height, PED square, and IRF square are different for groups with non-adherence and tear PED. Thus, the algorithm for the quantitative calculation of biomarkers provides more information for assessing the results of therapy, which can improve the outcomes of treatment in patients with $n$-AMD.