Journal Article

Artificial intelligence in atherosclerotic disease: Applications and trends

Tsarouchas, Anastasios, Eckstein, Hans-Henning, Karlas, Angelos, Hadjileontiadis, Leontios, Kampaktsis, Polydoros N., Bakogiannis, Constantinos, Mouselimis, Dimitrios, Emfietzoglou, Maria, Vassilikos, Vassilios P., Fasoula, Nikolina-Alexia, Al Shehhi, Aamna, Kallmayer, Michael

2023

Overview

Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: Algorithms / Artificial intelligence / Atherosclerosis / Automation / Big Data / Cardiovascular Medicine / carotid artery disease / Classification / Clinical medicine / Clustering / coronary artery disease / Coronary vessels / Decision making / Deep learning / Generalized linear models / Machine learning / Medical imaging / Neural networks / Patients / Pattern recognition / peripheral arterial disease / Regression analysis / Support vector machines