Application of Artificial Intelligence in Cardiovascular Diseases

Cardiovascular imaging techniques, including echocardiography, cardiac computed tomography angiography (CCTA), cardiac magnetic resonance (CMR), and nuclide cardiac imaging, have played an indispensable role in diagnosing and treating cardiovascular diseases in recent years. Compared with other organs within the body, the three-dimensional structure of the heart is relatively complex, comprising cardiac chambers, valves, myocardium, and other components. Moreover, the heart is an actively moving organ that exhibits periodic motion, characterized by a high beating rate. Therefore, cardiovascular imaging needs to account for the motion state and rhythm of the heart. Cardiovascular imaging data have spatiotemporal multidimensional features, which provide new opportunities for the increasing development of artificial intelligence (AI) methods in this field. The application of AI methods in the field of cardiovascular imaging currently covers examination and diagnostic processes. The innovation of AI methods in reconstruction algorithms and scanning techniques has significantly improved the image quality and imaging efficiency of cardiovascular imaging. In terms of diagnosing cardiovascular diseases, AI methods can achieve automatic extraction of coronary arteries, quantitative analysis of stenosis severity, segmentation of cardiac structures, and other techniques, thereby further improving coronary heart disease diagnosis accuracy. In the risk stratification for cardiovascular diseases, integrating radiological imaging and clinical information using AI techniques enables the identification of disease risks, optimization of risk stratification, prediction of long-term prognosis, and facilitation of personalized treatment approaches.