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

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• dc.contributor.author Sanchez-Martinez, Sergio
• dc.contributor.author Camara, Oscar
• dc.contributor.author Piella Fenoy, Gemma
• dc.contributor.author Cikes, Maja
• dc.contributor.author González Ballester, Miguel Ángel, 1973-
• dc.contributor.author Miron, Marius
• dc.contributor.author Vellido Alcacena, Alfredo
• dc.contributor.author Gómez, Emilia
• dc.contributor.author Fraser, Alan G.
• dc.contributor.author Bijnens, Bart
• dc.date.accessioned 2025-10-20T06:20:05Z
• dc.date.available 2025-10-20T06:20:05Z
• dc.date.issued 2022
• dc.description.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.en
• dc.description.sponsorship This study was supported by the Spanish Ministry of Economy and Competitiveness (María de Maeztu Programme for R&D [MDM-2015-0502], Madrid, Spain) and by the Fundació La Marató de TV3 (n°20154031, Barcelona, Spain). The work of SS-M was supported by IDIBAPS and by the HUMAINT project of the Joint Research Centre of the European Commission. AV's contribution is funded by Spanish research project PID2019-104551RB-I00.en
• dc.format.mimetype application/pdf
• dc.identifier.citation 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
• dc.identifier.doi http://dx.doi.org/10.3389/fcvm.2021.765693
• dc.identifier.issn 2297-055X
• dc.identifier.uri http://hdl.handle.net/10230/71558
• dc.language.iso eng
• dc.publisher Frontiers
• dc.relation.ispartof Frontiers in Cardiovascular Medicine. 2022 Jan 4;8:765693
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-104551RB-I00
• dc.rights © 2022 Sanchez-Martinez, Camara, Piella, Cikes, González-Ballester, Miron, Vellido, Gómez, Fraser and Bijnens. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Artificial intelligenceen
• dc.subject.keyword Machine learningen
• dc.subject.keyword Deep learningen
• dc.subject.keyword Clinical decision makingen
• dc.subject.keyword Cardiovascular imagingen
• dc.subject.keyword Diagnosisen
• dc.subject.keyword Predictionen
• dc.title Machine learning for clinical decision-making: challenges and opportunities in cardiovascular imagingen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion