Abstract
Acute coronary syndrome is the leading cause of cardiac death and has a significant impact on patient prognosis. Early identification and proper management are key to ensuring better outcomes and have improved significantly with the development of various cardiovascular imaging modalities. Recently, the use of artificial intelligence as a method of enhancing the capability of cardiovascular imaging has grown. AI can inform the decision-making process, as it enables existing modalities to perform more efficiently and make more accurate diagnoses. This review demonstrates recent applications of AI in cardiovascular imaging to facilitate better patient care.
Highlights
Acute coronary syndrome (ACS) is a common type of coronary artery disease, which often leads to devastating consequences [1–3]
Lin et al successfully identified acute myocardial infarction (AMI) by building a machine learning model that combined a series of clinical factors and pericoronary adipose tissue attenuation with CT radiomic to identify AMI patients, achieving an AUC of 0.87 [11]
We look forward to seeing what radiomics combining serum biomarkers can achieve
Summary
Acute coronary syndrome (ACS) is a common type of coronary artery disease, which often leads to devastating consequences [1–3]. Featuring CT- based plaque qualitatively and quantitatively, Al’ Aref et al identified precursors of culprit lesion (CL) in ACS patients who had CAG.
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