THE USE OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF ARTERIAL CALCIFICATION

Abstract

Justification. The incidence of diseases of the circulatory system of the population of the Russian Federation has been steadily increasing over the past two decades, increasing 2,047 times from 2000 to 2019. The process of vascular calcification implies the deposition of calcium salts in the artery wall, leading to remodeling of the vascular wall. Radiation research methods are the gold standard for the diagnosis of vascular calcification. However, due to the need for medical professionals to process a large amount of data for a certain period of time, the number of diagnostic errors inevitably increases, as well as the efficiency of work decreases. The active development and introduction of artificial intelligence (AI) into clinical practice has opened up opportunities for specialists to solve these problems. The purpose of the study. To analyze the domestic and foreign literature devoted to the use of AI in the diagnosis of various types of vascular calcification, as well as to summarize the prognostic value of vascular calcification and evaluate aspects that prevent the diagnosis of vascular calcification without the use of AI. Material and methods. The authors searched for publications in the electronic databases PubMed, Web of Science, Google Scholar and eLibrary. The search was carried out using the following keywords: "artificial intelligence", "machine learning", "vascular calcification", "artificial intelligence", "machine learning", "vascular calcification". The search was carried out in the time interval from the moment of the foundation of the corresponding database until July 2023. Conclusion. AI has proven itself well in the diagnosis of vascular calcification. In addition to improving accuracy and efficiency, the ability to detail surpasses the capabilities of the manual diagnostic method. AI has reached a level that allows doctors to help instrumental diagnostics in the automatic detection of vascular calcification. AI capabilities can contribute to the effective development of radiology in the future.