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

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.

SÁNCHEZ-MARTÍNEZ Sergio;  CÁMARA Oscar;  PIELLA Gemma;  CIKES Maya;  GONZÁLEZ-BALLESTER Miguel Angel;  MIRON Marius;  VELLIDO Aldredo;  GOMEZ GUTIERREZ Emilia;  FRASER Alan G.;  BIJNENS Bart; 

2022-04-06

FRONTIERS MEDIA SA

JRC126777

2297-055X (online),   

https://www.frontiersin.org/articles/10.3389/fcvm.2021.765693/full,    https://publications.jrc.ec.europa.eu/repository/handle/JRC126777,   

10.3389/fcvm.2021.765693 (online),