Machine learning in cardiac CT: Basic concepts and contemporary data.
J Cardiovasc Comput Tomogr. 2018 May - Jun;12(3):192-201. doi: 10.1016/j.jcct.2018.04.010. Epub 2018 Apr 30. Singh G1, Al'Aref SJ1, Van Assen M2, Kim TS1, van Rosendael A1, Kolli KK1, Dwivedi A1, Maliakal G1, Pandey M1, Wang J1, Do V1, Gummalla M1, De Cecco CN3, Min JK4.
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.