TY - JOUR
T1 - Artificial Intelligence Based Multimodality Imaging
T2 - A New Frontier in Coronary Artery Disease Management
AU - Maragna, Riccardo
AU - Giacari, Carlo Maria
AU - Guglielmo, Marco
AU - Baggiano, Andrea
AU - Fusini, Laura
AU - Guaricci, Andrea Igoren
AU - Rossi, Alexia
AU - Rabbat, Mark
AU - Pontone, Gianluca
N1 - Publisher Copyright:
© 2021 Maragna, Giacari, Guglielmo, Baggiano, Fusini, Guaricci, Rossi, Rabbat and Pontone.
PY - 2021
Y1 - 2021
N2 - Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.
AB - Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.
KW - artificial intelligence
KW - coronary artery disease
KW - deep learning
KW - machine learning
KW - multimodality imaging
KW - radiomics
UR - http://www.scopus.com/inward/record.url?scp=85124120880&partnerID=8YFLogxK
U2 - 10.3389/fcvm.2021.736223
DO - 10.3389/fcvm.2021.736223
M3 - Review article
AN - SCOPUS:85124120880
SN - 2297-055X
VL - 8
JO - Frontiers in Cardiovascular Medicine
JF - Frontiers in Cardiovascular Medicine
M1 - 736223
ER -