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Cardiac Phase Space Analysis: Assessing Coronary Artery Disease Utilizing Artificial Intelligence

The bridge of artificial intelligence to cardiovascular medicine has opened up new avenues for novel diagnostics that may significantly enhance the cardiology care pathway. Cardiac phase space analysis is a noninvasive diagnostic platform that combines advanced disciplines of mathematics and physics...

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Detalles Bibliográficos
Autores principales: Rabbat, Mark G., Ramchandani, Shyam, Sanders, William E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8053062/
https://www.ncbi.nlm.nih.gov/pubmed/33928155
http://dx.doi.org/10.1155/2021/6637039
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author Rabbat, Mark G.
Ramchandani, Shyam
Sanders, William E.
author_facet Rabbat, Mark G.
Ramchandani, Shyam
Sanders, William E.
author_sort Rabbat, Mark G.
collection PubMed
description The bridge of artificial intelligence to cardiovascular medicine has opened up new avenues for novel diagnostics that may significantly enhance the cardiology care pathway. Cardiac phase space analysis is a noninvasive diagnostic platform that combines advanced disciplines of mathematics and physics with machine learning. Thoracic orthogonal voltage gradient (OVG) signals from an individual are evaluated by cardiac phase space analysis to quantify physiological and mathematical features associated with coronary stenosis. The analysis is performed at the point of care without the need for a change in physiologic status or radiation. This review will highlight some of the scientific principles behind the technology, provide a description of the system and device, and discuss the study procedure, clinical data, and potential future applications.
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spelling pubmed-80530622021-04-28 Cardiac Phase Space Analysis: Assessing Coronary Artery Disease Utilizing Artificial Intelligence Rabbat, Mark G. Ramchandani, Shyam Sanders, William E. Biomed Res Int Review Article The bridge of artificial intelligence to cardiovascular medicine has opened up new avenues for novel diagnostics that may significantly enhance the cardiology care pathway. Cardiac phase space analysis is a noninvasive diagnostic platform that combines advanced disciplines of mathematics and physics with machine learning. Thoracic orthogonal voltage gradient (OVG) signals from an individual are evaluated by cardiac phase space analysis to quantify physiological and mathematical features associated with coronary stenosis. The analysis is performed at the point of care without the need for a change in physiologic status or radiation. This review will highlight some of the scientific principles behind the technology, provide a description of the system and device, and discuss the study procedure, clinical data, and potential future applications. Hindawi 2021-04-09 /pmc/articles/PMC8053062/ /pubmed/33928155 http://dx.doi.org/10.1155/2021/6637039 Text en Copyright © 2021 Mark G. Rabbat et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Rabbat, Mark G.
Ramchandani, Shyam
Sanders, William E.
Cardiac Phase Space Analysis: Assessing Coronary Artery Disease Utilizing Artificial Intelligence
title Cardiac Phase Space Analysis: Assessing Coronary Artery Disease Utilizing Artificial Intelligence
title_full Cardiac Phase Space Analysis: Assessing Coronary Artery Disease Utilizing Artificial Intelligence
title_fullStr Cardiac Phase Space Analysis: Assessing Coronary Artery Disease Utilizing Artificial Intelligence
title_full_unstemmed Cardiac Phase Space Analysis: Assessing Coronary Artery Disease Utilizing Artificial Intelligence
title_short Cardiac Phase Space Analysis: Assessing Coronary Artery Disease Utilizing Artificial Intelligence
title_sort cardiac phase space analysis: assessing coronary artery disease utilizing artificial intelligence
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8053062/
https://www.ncbi.nlm.nih.gov/pubmed/33928155
http://dx.doi.org/10.1155/2021/6637039
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