Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

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...

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Published inFrontiers in cardiovascular medicine Vol. 8; p. 736223
Main Authors Maragna, Riccardo, Giacari, Carlo Maria, Guglielmo, Marco, Baggiano, Andrea, Fusini, Laura, Guaricci, Andrea Igoren, Rossi, Alexia, Rabbat, Mark, Pontone, Gianluca
Format Journal Article
LanguageEnglish
Published Frontiers Media S.A 22.09.2021
Subjects
artificial intelligence
Cardiovascular Medicine
coronary artery disease
deep learning
machine learning
multimodality imaging
radiomics
Online AccessGet full text
ISSN2297-055X
2297-055X
DOI10.3389/fcvm.2021.736223

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Abstract 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.
AbstractList 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.
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.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.
Author Maragna, Riccardo
Rabbat, Mark
Rossi, Alexia
Guaricci, Andrea Igoren
Fusini, Laura
Baggiano, Andrea
Guglielmo, Marco
Pontone, Gianluca
Giacari, Carlo Maria
AuthorAffiliation 1 Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) , Milan , Italy
3 Department of Emergency and Organ Transplantation, Institute of Cardiovascular Disease, University Hospital Policlinico of Bari , Bari , Italy
5 Center for Molecular Cardiology, University Hospital Zurich , Zurich , Switzerland
6 Department of Medicine and Radiology, Division of Cardiology, Loyola University of Chicago , Chicago, IL , United States
7 Department of Medicine, Division of Cardiology, Edward Hines Jr. VA Hospital , Hines, IL , United States
2 Department of Clinical Sciences and Community Health, Cardiovascular Section, University of Milan , Milan , Italy
4 Department of Nuclear Medicine, University Hospital Zurich , Zurich , Switzerland
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– name: 6 Department of Medicine and Radiology, Division of Cardiology, Loyola University of Chicago , Chicago, IL , United States
– name: 3 Department of Emergency and Organ Transplantation, Institute of Cardiovascular Disease, University Hospital Policlinico of Bari , Bari , Italy
– name: 1 Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) , Milan , Italy
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Copyright Copyright © 2021 Maragna, Giacari, Guglielmo, Baggiano, Fusini, Guaricci, Rossi, Rabbat and Pontone.
Copyright © 2021 Maragna, Giacari, Guglielmo, Baggiano, Fusini, Guaricci, Rossi, Rabbat and Pontone. 2021 Maragna, Giacari, Guglielmo, Baggiano, Fusini, Guaricci, Rossi, Rabbat and Pontone
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Reviewed by: Alexandros Kallifatidis, St. Luke's Hospital, Greece; Anastasios Panagopoulos, University of Nebraska Medical Center, United States
This article was submitted to Cardiovascular Imaging, a section of the journal Frontiers in Cardiovascular Medicine
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Snippet Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both...
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SubjectTerms artificial intelligence
Cardiovascular Medicine
coronary artery disease
deep learning
machine learning
multimodality imaging
radiomics
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Title Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management
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