Cerebral vascular pathologies are a widespread and socially significant disease. In particular, cerebral aneurysms can lead to stroke, which ranks second in mortality in the world according to WHO. Large-scale studies conducted over the past decade in the United States show that the existing criteria for making medical decisions (to perform surgery or to observe conservatively) have weak statistical significance, which prompts many researchers to deal with this problem: preoperative modeling of the treatment of cerebral aneurysms. At the same time, the development of mathematical models, as well as the creation of software complexes, can be based only on a high-quality and large-scale array of experimental data.In this study, we consider two approaches that we use to obtain personalized data on vascular pathology, as well as mathematical and numerical modeling based on such experimental data.
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