Machine learning in cardiac CT: Basic concepts and contemporary dataSingh, G., Al'Aref, S. J., Van Assen, M., Kim, T. S., van Rosendael, A., Kolli, K. K., Dwivedi, A., Maliakal, G., Pandey, M., Wang, J., Do, V., Gummalla, M., De Cecco, C. N. & Min, J. K., May-2018, In : Journal of Cardiovascular Computed Tomography. 12, 3, p. 192-201 10 p.
Research output: Contribution to journal › Review article › Academic › peer-review
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.
|Number of pages||10|
|Journal||Journal of Cardiovascular Computed Tomography|
|Publication status||Published - May-2018|
- 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