The application of AI in interventional cardiology enhances diagnostic accuracy, reduces risks, and optimizes treatment procedures, particularly through machine learning algorithms.
AI technologies, including machine learning and deep learning, offer significant potential to improve diagnostic accuracy, risk assessment, and procedural optimization in interventional cardiology.
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A scientific review, which summarizes and critically interprets previously published information posted in Scopus, Web of Science, PubMed, eLibrary.ru, CyberLeninka databases on the application of artificial intelligence (AI) technologies in the diagnostic process, treatment, prediction of cardiovascular diseases and optimization of intervention procedures, was presented. Search period — 8 years. Real application of AI in clinical practice has been considered. It has been shown that the use of AI based on machine learning and deep learning algorithms offers unique opportunities for analyzing large volumes of medical data, interpreting the results of instrumental research methods (echocardiography, electrocardiography, computed tomography angiography, computed tomography of the heart, magnetic resonance imaging) and assessment of the risk of adverse cardiovascular events. Machine learning methods can complement and extend the traditional statistical methods of AI algorithms. Deep learning is a subdomain of machine learning and is characterized by algorithms that are based on the principle of human brain work, including a class of algorithms called neural networks. Artificial, recurrent and convolutional neural networks have been used in interventional cardiology. Artificial neural networks can be used in robotic systems and neural interfaces, providing energy-efficient real-time signal processing. Convolutional networks are used for medical image processing, assisting in organ segmentation, pathology detection and navigation during operations, and recurrent networks — for analysis of the dynamic indicators of data and prediction of complications. Together, these technologies improve diagnostic accuracy, reduce risk and optimize intervention course. Thus, the introduction of AI in the interventional cardiology opens new horizons for diagnosis, treatment and prediction of cardiovascular diseases. The high efficiency of modern machine learning algorithms in analyzing the results of instrumental research methods, processing large amounts of data and detecting genetic markers of cardiovascular diseases has been shown. However, the introduction of AI into interventional cardiology faces a number of challenges despite significant progress. In the long term, AI is expected to become an integral part of interventional cardiology, making treatment more accurate, safe and affordable.
Kalyuta et al. (Sun,) reported a other. The application of AI in interventional cardiology enhances diagnostic accuracy, reduces risks, and optimizes treatment procedures, particularly through machine learning algorithms.