Machine Learning Applied to Support Medical Decision in Transthoracic Echocardiogram Exams: A Systematic Review

Siqueira, Vilson Soares de; Rodrigues, Diego de Castro; Dourado, Colandy Nunes; Borges, Moises Marcos; Furtado, Rogério Gomes; Delfino, Higor Pereira; Stelle, Diego; Barbosa, Rommel M.; Costa, Ronaldo Martins da

doi:10.1109/compsac48688.2020.0-215

Cited by 4 publications

(1 citation statement)

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“…In diabetic retinopathy diagnosis ( 83 ), model assistance increased the accuracy of retina specialists above that of the unassisted reader or model alone. In cardiovascular imaging, most examples of human-machine collaboration thus far focus on segmentation, and detection-classification of imaging planes ( 84 ).…”

Section: General Challengesmentioning

confidence: 99%

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

Sanchez-Martinez

Cámara

Piella

et al. 2022

Front. Cardiovasc. Med.

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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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Section: General Challengesmentioning

confidence: 99%