Use of artificial intelligence in the diagnosis of arterial calcification

• Published in: Digital diagnostics 2024-04, Vol.5 (1), p.85-100
• Main Authors: Trusov, Yuri А., Chupakhina, Victoria S., Nurkaeva, Adilya S., Yakovenko, Natalia A., Ablenina, Irina V., Latypova, Roksana F., Pitke, Aleksandra P., Yazovskih, Anastasiya A., Ivanov, Artem S., Bogatyreva, Darya S., Popova, Ulyana A., Yuzlekbaev, Azat F.
• Format: Article
• Language: English
• ISSN: 2712-8490; 2712-8962
• DOI: 10.17816/DD623196

BACKGROUND: The incidence of circulatory system diseases in the Russian Federation has been steadily increasing during the last two decades, growing 2,047 times between 2000 and 2019. Vascular calcification involves the deposition of calcium salts in the artery wall, which leads to vascular wall remodeling. X-ray imaging is the gold standard for diagnosing of vascular calcification. However, because of the need to process an increasing amount of data in a shorter period of time, the number of diagnostic errors inevitably increases, and work efficiency inevitably decreases. The active development and introduction of artificial intelligence into clinical practice have created opportunities for specialists to address these issues. AIM: To analyze the national and international literature on the use of artificial intelligence in the diagnosis of various vascular calcifications, summarize the prognostic value of vascular calcification, and evaluate aspects that prevent the diagnosis of vascular calcification without using artificial intelligence. MATERIALS AND METHODS: A search was performed in PubMed, Web of Science, Google Scholar, and eLibrary. The search was conducted using the following keywords: artificial intelligence, machine learning, vascular calcification, and their analogues in Russian. The search covered the period from inception till July 2023. RESULTS: The studies included in the review compared the diagnostic abilities of clinicians and artificial intelligence using the same images, with subsequent assessment of the accuracy, speed, and other parameters. The sites of vascular calcification varied, resulting in differences in their prognostic value. CONCLUSION: Artificial intelligence has proven to be effective in the diagnosis of vascular calcification. In addition to improved accuracy and efficiency, the level of detail is superior to manual diagnosis methods. Artificial intelligence has advanced to the point that imaging specialists can automatically detect vascular calcification. Artificial intelligence can contribute to the successful development of X-ray imaging in the future.