Despite their relatively low prevalence compared to cardiac valve lesions and coronary heart disease, thoracic aortic aneurysm and dissection are potentially fatal and represent serious public health problems. The indications for surgical treatment in most thoracic aortic diseases are predominantly based on the maximum aortic diameter in a particular area. Congenital connective tissue disorder, thoracic aortic anomalies (e.g., coarctation), family history of aneurysms, aortic dissections, and sudden deaths are considered as additional risk factors of aortic-related complications influencing the “stricter” indications and lowering the “threshold” aortic diameter. At the same time, a certain proportion of patients with aortic diseases develop aortic dissection and rupture in normal or near-normal thoracic aortic diameter in certain section. Many factors influence the development of aortic diseases and complications, and assessing the contribution to the aetiology and pathogenesis of each factor is difficult. Machine learning and mathematical modeling using artificial intelligence is an actively developing area of computer science, which also finds application in medicine, in particular in the study, diagnosis, and treatment of thoracic aortic aneurysms and dissections. This article discusses modern methods of data analysis, prediction of thoracic aortic aneurysms and dissections, treatment planning in thoracic aortic diseases, and prediction of complications using machine learning and artificial intelligence.

Despite their relatively low prevalence compared to cardiac valve lesions and coronary heart disease, thoracic aortic aneurysm and dissection are potentially fatal and represent serious public health problems. The indications for surgical treatment in most thoracic aortic diseases are predominantly based on the maximum aortic diameter in a particular area. Congenital connective tissue disorder, thoracic aortic anomalies (e.g., coarctation), family history of aneurysms, aortic dissections, and sudden deaths are considered as additional risk factors of aortic-related complications influencing the “stricter” indications and lowering the “threshold” aortic diameter. At the same time, a certain proportion of patients with aortic diseases develop aortic dissection and rupture in normal or near-normal thoracic aortic diameter in certain section. Many factors influence the development of aortic diseases and complications, and assessing the contribution to the aetiology and pathogenesis of each factor is difficult. Machine learning and mathematical modeling using artificial intelligence is an actively developing area of computer science, which also finds application in medicine, in particular in the study, diagnosis, and treatment of thoracic aortic aneurysms and dissections. This article discusses modern methods of data analysis, prediction of thoracic aortic aneurysms and dissections, treatment planning in thoracic aortic diseases, and prediction of complications using machine learning and artificial intelligence.

Федеральное государственное бюджетное учреждение «Национальный медицинский исследовательский центр имени В. А. Алмазова» Министерства здравоохранения Российской Федерации, Научный центр мирового уровня «Центр персонализированной медицины»,