Machine learning for clinical decision-making: challenges and opportunities in cardiovascular imaging

Sanchez-Martinez, Sergio; Camara, Oscar; Piella Fenoy, Gemma; Cikes, Maja; González Ballester, Miguel Ángel, 1973-; Miron, Marius; Vellido Alcacena, Alfredo; Gómez, Emilia; Fraser, Alan G.; Bijnens, Bart

doi:http://dx.doi.org/10.3389/fcvm.2021.765693

Machine learning for clinical decision-making: challenges and opportunities in cardiovascular imaging

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Sanchez-Martinez S, Camara O, Piella G, Cikes M, González-Ballester MÁ, Miron M, et al. Machine learning for clinical decision-making: challenges and opportunities in cardiovascular imaging. Front Cardiovasc Med. 2022 Jan 4;8:765693. DOI: 10.3389/fcvm.2021.765693

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.