AUTHOR=Sanchez-Martinez Sergio , Camara Oscar , Piella Gemma , Cikes Maja , González-Ballester Miguel Ángel , Miron Marius , Vellido Alfredo , Gómez Emilia , Fraser Alan G. , Bijnens Bart TITLE=Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging JOURNAL=Frontiers in Cardiovascular Medicine VOLUME=8 YEAR=2022 URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2021.765693 DOI=10.3389/fcvm.2021.765693 ISSN=2297-055X ABSTRACT=

The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making in cardiology. The success of these tools is dependent on the understanding of the intrinsic processes being used during the conventional pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous steps to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with the two later tasks of interpretation and decision support, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes in cardiovascular imaging.