부산대학교 도서관

Echocardiography frequently serves as the first-line treatment of diagnostic imaging for several pathological entities in cardiology. Artificial intelligence (AI) has been growing substantially in information technology and various commercial industries. Machine learning (ML), a branch of AI, has been shown to expand the capabilities and potential of echocardiography. ML algorithms expand the field of echocardiography by automated assessment of the ejection fraction and left ventricular function, integrating novel approaches such as speckle tracking or tissue Doppler echocardiography or vector flow mapping, improved phenotyping, distinguishing between cardiac conditions, and incorporating information from mobile health and genomics. In this review article, we assess the impact of AI and ML in echocardiography. Plain Language Summary: Echocardiography is the most common test in cardiovascular imaging and helps diagnose multiple different diseases. Machine learning, a branch of artificial intelligence (AI), will reduce the workload for medical professionals and help improve clinical workflows. It can rapidly calculate a lot of important cardiac parameters such as the ejection fraction or important metrics during different phases of the cardiac cycle. Machine learning algorithms can include new technology in echocardiography such as speckle tracking, tissue Doppler echocardiography, vector flow mapping, and other approaches in a user-friendly manner. Furthermore, it can help find new subtypes of existing diseases in cardiology. In this review article, we look at the current role of machine learning and AI in the field of echocardiography. [ABSTRACT FROM AUTHOR]