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

Znanstveni rad - Pregledni rad

• Autor: Sergio Sanchez-Martinez
• Autor: Oscar Camara
• Autor: Gemma Piella
• Autor: Maja Čikes
• Autor: Miguel Ángel González-Ballester
• Autor: Marius Miron
• Autor: Alfredo Vellido
• Autor: Emilia Gómez
• Autor: Alan G. Fraser
• Autor: Bart Bijnens
• Autorova ustanova: Sveučilište u Zagrebu; Medicinski fakultet; (KATEDRA ZA INTERNU MEDICINU)
• Znanstveno / umjetničko područje, polje i grana: BIOMEDICINA I ZDRAVSTVO; Kliničke medicinske znanosti; Intenzivna medicina

Sažetak (engleski)

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.

• Ključne riječi (engleski): artificial intelligence; cardiovascular imaging; clinical decision making; deep learning; diagnosis; machine learning; prediction
• Jezik: engleski
• Vrsta publikacije: Znanstveni rad - Pregledni rad
• Status objave: Objavljen
• Vrsta recenzije: Recenziran
• Verzija publikacije: Objavljena verzija rada (izdavačev PDF)
• Naslov časopisa: Frontiers in Cardiovascular Medicine
• Brojčani podaci: vol. 8, 765693
• e-ISSN: 2297-055X
• DOI: https://doi.org/10.3389/fcvm.2021.765693
• URN:NBN: urn:nbn:hr:105:785880
• Datum objave publikacije: 2022-01-04
• URL dokumenta: https://www.frontiersin.org/articles/10.3389/fcvm.2021.765693/full
• Vrsta resursa: Tekst
• Prava pristupa: Otvoreni pristup
• Datum i vrijeme pohrane: 2022-06-30 07:41:44