REVIEW  ARTIFICIAL INTELLIGENCE IN CARDIOLOGY 

Minerva Cardiology and Angiology 2022 February;70(1):75-91

DOI: 10.23736/S2724-5683.21.05709-4

Copyright © 2021 EDIZIONI MINERVA MEDICA

lingua: Inglese

Machine learning for cardiology

Yasir ARFAT ¹ ✉, Gianluca MITTONE ¹, Roberto ESPOSITO ¹, Barbara CANTALUPO ¹, Gaetano M. DE FERRARI ^{2, 3}, Marco ALDINUCCI ¹

¹ Department of Computer Science, University of Turin, Turin, Italy; ² Division of Cardiology, Department of Cardiovascular and Thoracic, Molinette Hospital, Città della Salute e della Scienza, Turin, Italy; ³ Unit of Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy

This paper reviews recent cardiology literature and reports how artificial intelligence tools (specifically, machine learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in machine learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying machine learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while neural networks are slowly being incorporated in cardiovascular research, other important techniques such as semi-supervised learning and federated learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.

KEY WORDS: Cardiology; Machine learning; Risk factors; Statistics; Mortality