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Artificial Intelligence—A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome

Acute coronary syndrome is the leading cause of cardiac death and has a significant impact on patient prognosis. Early identification and proper management are key to ensuring better outcomes and have improved significantly with the development of various cardiovascular imaging modalities. Recently,...

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Detalles Bibliográficos
Autores principales: Liu, Ming-hao, Zhao, Chen, Wang, Shengfang, Jia, Haibo, Yu, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8888682/
https://www.ncbi.nlm.nih.gov/pubmed/35252367
http://dx.doi.org/10.3389/fcvm.2021.782971
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author Liu, Ming-hao
Zhao, Chen
Wang, Shengfang
Jia, Haibo
Yu, Bo
author_facet Liu, Ming-hao
Zhao, Chen
Wang, Shengfang
Jia, Haibo
Yu, Bo
author_sort Liu, Ming-hao
collection PubMed
description Acute coronary syndrome is the leading cause of cardiac death and has a significant impact on patient prognosis. Early identification and proper management are key to ensuring better outcomes and have improved significantly with the development of various cardiovascular imaging modalities. Recently, the use of artificial intelligence as a method of enhancing the capability of cardiovascular imaging has grown. AI can inform the decision-making process, as it enables existing modalities to perform more efficiently and make more accurate diagnoses. This review demonstrates recent applications of AI in cardiovascular imaging to facilitate better patient care.
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spelling pubmed-88886822022-03-03 Artificial Intelligence—A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome Liu, Ming-hao Zhao, Chen Wang, Shengfang Jia, Haibo Yu, Bo Front Cardiovasc Med Cardiovascular Medicine Acute coronary syndrome is the leading cause of cardiac death and has a significant impact on patient prognosis. Early identification and proper management are key to ensuring better outcomes and have improved significantly with the development of various cardiovascular imaging modalities. Recently, the use of artificial intelligence as a method of enhancing the capability of cardiovascular imaging has grown. AI can inform the decision-making process, as it enables existing modalities to perform more efficiently and make more accurate diagnoses. This review demonstrates recent applications of AI in cardiovascular imaging to facilitate better patient care. Frontiers Media S.A. 2022-02-16 /pmc/articles/PMC8888682/ /pubmed/35252367 http://dx.doi.org/10.3389/fcvm.2021.782971 Text en Copyright © 2022 Liu, Zhao, Wang, Jia and Yu. 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
Liu, Ming-hao
Zhao, Chen
Wang, Shengfang
Jia, Haibo
Yu, Bo
Artificial Intelligence—A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome
title Artificial Intelligence—A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome
title_full Artificial Intelligence—A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome
title_fullStr Artificial Intelligence—A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome
title_full_unstemmed Artificial Intelligence—A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome
title_short Artificial Intelligence—A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome
title_sort artificial intelligence—a good assistant to multi-modality imaging in managing acute coronary syndrome
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8888682/
https://www.ncbi.nlm.nih.gov/pubmed/35252367
http://dx.doi.org/10.3389/fcvm.2021.782971
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