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

Autori
Sanchez-Martinez S, Camara O, Piella G, Cikes M, González-Ballester MÁ, Miron M, Vellido A, Gómez E, Fraser AG, Bijnens B

Izvornik
Frontiers in cardiovascular medicine (2297-055X) 8 (2022); 765693, 11

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
artificial intelligence ; cardiovascular imaging ; clinical decision making ; deep learning ; diagnosis ; machine learning ; prediction

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Kliničke medicinske znanosti

POVEZANOST RADA