Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging

Scientific paper - Review paper

• Author: Sergio Sanchez-Martinez
• Author: Oscar Camara
• Author: Gemma Piella
• Author: Maja Čikes
• Author: Miguel Ángel González-Ballester
• Author: Marius Miron
• Author: Alfredo Vellido
• Author: Emilia Gómez
• Author: Alan G. Fraser
• Author: Bart Bijnens
• Author's institution: University of Zagreb; School of Medicine; (Department of Internal Medicine)
• Scientific / art field, discipline and subdiscipline: BIOMEDICINE AND HEALTHCARE; Clinical Medical Sciences; Intensive Care

Abstract (english)

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.

• Keywords (english): artificial intelligence; cardiovascular imaging; clinical decision making; deep learning; diagnosis; machine learning; prediction
• Language: english
• Publication type: Scientific paper - Review paper
• Publication status: Published
• Peer review: Peer review
• Publication version: Published version
• Journal title: Frontiers in Cardiovascular Medicine
• Numbering: vol. 8, 765693
• e-ISSN: 2297-055X
• DOI: https://doi.org/10.3389/fcvm.2021.765693
• URN:NBN: urn:nbn:hr:105:785880
• Publication: 2022-01-04
• Document URL: https://www.frontiersin.org/articles/10.3389/fcvm.2021.765693/full
• Type of resource: Text
• Access conditions: Open access
• Created on: 2022-06-30 07:41:44