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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8493089/ https://www.ncbi.nlm.nih.gov/pubmed/34631834 http://dx.doi.org/10.3389/fcvm.2021.736223 |
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author | Maragna, Riccardo Giacari, Carlo Maria Guglielmo, Marco Baggiano, Andrea Fusini, Laura Guaricci, Andrea Igoren Rossi, Alexia Rabbat, Mark Pontone, Gianluca |
author_facet | Maragna, Riccardo Giacari, Carlo Maria Guglielmo, Marco Baggiano, Andrea Fusini, Laura Guaricci, Andrea Igoren Rossi, Alexia Rabbat, Mark Pontone, Gianluca |
author_sort | Maragna, Riccardo |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8493089 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84930892021-10-07 Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management Maragna, Riccardo Giacari, Carlo Maria Guglielmo, Marco Baggiano, Andrea Fusini, Laura Guaricci, Andrea Igoren Rossi, Alexia Rabbat, Mark Pontone, Gianluca Front Cardiovasc Med Cardiovascular Medicine 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. Frontiers Media S.A. 2021-09-22 /pmc/articles/PMC8493089/ /pubmed/34631834 http://dx.doi.org/10.3389/fcvm.2021.736223 Text en Copyright © 2021 Maragna, Giacari, Guglielmo, Baggiano, Fusini, Guaricci, Rossi, Rabbat and Pontone. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cardiovascular Medicine Maragna, Riccardo Giacari, Carlo Maria Guglielmo, Marco Baggiano, Andrea Fusini, Laura Guaricci, Andrea Igoren Rossi, Alexia Rabbat, Mark Pontone, Gianluca Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management |
title | Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management |
title_full | Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management |
title_fullStr | Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management |
title_full_unstemmed | Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management |
title_short | Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management |
title_sort | artificial intelligence based multimodality imaging: a new frontier in coronary artery disease management |
topic | Cardiovascular Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8493089/ https://www.ncbi.nlm.nih.gov/pubmed/34631834 http://dx.doi.org/10.3389/fcvm.2021.736223 |
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