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Role of Deep Learning in Computed Tomography

Computed tomography has played an instrumental role in the understanding of the pathophysiology of atherosclerosis in coronary artery disease. It enables visualization of the plaque obstruction and vessel stenosis in a comprehensive manner. As technology for computed tomography is constantly evolvin...

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Detalles Bibliográficos
Autores principales: Garg, Yash, Seetharam, Karthik, Sharma, Manjari, Rohita, Dipesh K, Nabi, Waseem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cureus 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275744/
https://www.ncbi.nlm.nih.gov/pubmed/37332431
http://dx.doi.org/10.7759/cureus.39160
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author Garg, Yash
Seetharam, Karthik
Sharma, Manjari
Rohita, Dipesh K
Nabi, Waseem
author_facet Garg, Yash
Seetharam, Karthik
Sharma, Manjari
Rohita, Dipesh K
Nabi, Waseem
author_sort Garg, Yash
collection PubMed
description Computed tomography has played an instrumental role in the understanding of the pathophysiology of atherosclerosis in coronary artery disease. It enables visualization of the plaque obstruction and vessel stenosis in a comprehensive manner. As technology for computed tomography is constantly evolving, coronary applications and possibilities are constantly expanding. This influx of information can overwhelm a physician's ability to interpret information in this era of big data. Machine learning is a revolutionary approach that can help provide limitless pathways in patient management. Within these machine algorithms, deep learning has tremendous potential and can revolutionize computed tomography and cardiovascular imaging. In this review article, we highlight the role of deep learning in various aspects of computed tomography. 
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spelling pubmed-102757442023-06-18 Role of Deep Learning in Computed Tomography Garg, Yash Seetharam, Karthik Sharma, Manjari Rohita, Dipesh K Nabi, Waseem Cureus Cardiology Computed tomography has played an instrumental role in the understanding of the pathophysiology of atherosclerosis in coronary artery disease. It enables visualization of the plaque obstruction and vessel stenosis in a comprehensive manner. As technology for computed tomography is constantly evolving, coronary applications and possibilities are constantly expanding. This influx of information can overwhelm a physician's ability to interpret information in this era of big data. Machine learning is a revolutionary approach that can help provide limitless pathways in patient management. Within these machine algorithms, deep learning has tremendous potential and can revolutionize computed tomography and cardiovascular imaging. In this review article, we highlight the role of deep learning in various aspects of computed tomography.  Cureus 2023-05-17 /pmc/articles/PMC10275744/ /pubmed/37332431 http://dx.doi.org/10.7759/cureus.39160 Text en Copyright © 2023, Garg et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Cardiology
Garg, Yash
Seetharam, Karthik
Sharma, Manjari
Rohita, Dipesh K
Nabi, Waseem
Role of Deep Learning in Computed Tomography
title Role of Deep Learning in Computed Tomography
title_full Role of Deep Learning in Computed Tomography
title_fullStr Role of Deep Learning in Computed Tomography
title_full_unstemmed Role of Deep Learning in Computed Tomography
title_short Role of Deep Learning in Computed Tomography
title_sort role of deep learning in computed tomography
topic Cardiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275744/
https://www.ncbi.nlm.nih.gov/pubmed/37332431
http://dx.doi.org/10.7759/cureus.39160
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