TY - JOUR
T1 - Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT
T2 - A Survey
AU - Hampe, Nils
AU - Wolterink, Jelmer M.
AU - van Velzen, Sanne G.M.
AU - Leiner, Tim
AU - Išgum, Ivana
PY - 2019/11/26
Y1 - 2019/11/26
N2 - Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.
AB - Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.
KW - Atherosclerotic plaque
KW - Cardiac CT
KW - Coronary artery disease
KW - Coronary artery stenosis
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85079425197&partnerID=8YFLogxK
U2 - 10.3389/fcvm.2019.00172
DO - 10.3389/fcvm.2019.00172
M3 - Review article
AN - SCOPUS:85079425197
SN - 2297-055X
VL - 6
JO - Frontiers in Cardiovascular Medicine
JF - Frontiers in Cardiovascular Medicine
M1 - 172
ER -
