Machine learning in cardiac CT: Basic concepts and contemporary data

Gurpreet Singh, Subhi J. Al'Aref, Marly Van Assen, Timothy Suyong Kim, Alexander van Rosendael, Kranthi K. Kolli, Aeshita Dwivedi, Gabriel Maliakal, Mohit Pandey, Jing Wang, Virginie Do, Manasa Gummalla, Carlo N. De Cecco, James K. Min*

*Corresponding author for this work

    Research output: Contribution to journalReview articlepeer-review

    42 Citations (Scopus)

    Abstract

    Propelled by the synergy of the groundbreaking advancements in the ability to analyze high-dimensional datasets and the increasing availability of imaging and clinical data, machine learning (ML) is poised to transform the practice of cardiovascular medicine. Owing to the growing body of literature validating both the diagnostic performance as well as the prognostic implications of anatomic and physiologic findings, coronary computed tomography angiography (CCTA) is now a well-established non-invasive modality for the assessment of cardiovascular disease. ML has been increasingly utilized to optimize performance as well as extract data from CCTA as well as non-contrast enhanced cardiac CT scans. The purpose of this review is to describe the contemporary state of ML based algorithms applied to cardiac CT, as well as to provide clinicians with an understanding of its benefits and associated limitations.

    Original languageEnglish
    Pages (from-to)192-201
    Number of pages10
    JournalJournal of Cardiovascular Computed Tomography
    Volume12
    Issue number3
    DOIs
    Publication statusPublished - May-2018

    Keywords

    • Machine learning
    • Computed tomography
    • Coronary artery calcium
    • Diagnostic performance
    • CORONARY-ARTERY-DISEASE
    • COMPUTED-TOMOGRAPHY ANGIOGRAPHY
    • FRACTIONAL FLOW RESERVE
    • CARDIOVASCULAR RISK-ASSESSMENT
    • AMERICAN-HEART-ASSOCIATION
    • APPROPRIATE USE CRITERIA
    • ALL-CAUSE MORTALITY
    • MYOCARDIAL-PERFUSION
    • MAGNETIC-RESONANCE
    • DIAGNOSTIC PERFORMANCE

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