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Sanchez-Martinez S, Camara O, Piella G, Cikes M, González-Ballester MÁ, Miron M, Vellido A, Gómez E, Fraser AG, Bijnens B. Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging. Front Cardiovasc Med 2022; 8:765693. [PMID: 35059445 PMCID: PMC8764455 DOI: 10.3389/fcvm.2021.765693] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 12/07/2021] [Indexed: 11/30/2022] Open
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.
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Affiliation(s)
| | - Oscar Camara
- Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona, Spain
| | - Gemma Piella
- Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona, Spain
| | - Maja Cikes
- Department of Cardiovascular Diseases, University of Zagreb School of Medicine, University Hospital Centre Zagreb, Zagreb, Croatia
| | | | - Marius Miron
- Joint Research Centre, European Commission, Seville, Spain
| | - Alfredo Vellido
- Computer Science Department, Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Emilia Gómez
- Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona, Spain
- Joint Research Centre, European Commission, Seville, Spain
| | - Alan G. Fraser
- School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Bart Bijnens
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain
- ICREA, Barcelona, Spain
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
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