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Artificial intelligence in the diagnosis and management of arrhythmias
The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intell...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497074/ https://www.ncbi.nlm.nih.gov/pubmed/34392353 http://dx.doi.org/10.1093/eurheartj/ehab544 |
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author | Nagarajan, Venkat D Lee, Su-Lin Robertus, Jan-Lukas Nienaber, Christoph A Trayanova, Natalia A Ernst, Sabine |
author_facet | Nagarajan, Venkat D Lee, Su-Lin Robertus, Jan-Lukas Nienaber, Christoph A Trayanova, Natalia A Ernst, Sabine |
author_sort | Nagarajan, Venkat D |
collection | PubMed |
description | The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care. |
format | Online Article Text |
id | pubmed-8497074 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-84970742021-10-08 Artificial intelligence in the diagnosis and management of arrhythmias Nagarajan, Venkat D Lee, Su-Lin Robertus, Jan-Lukas Nienaber, Christoph A Trayanova, Natalia A Ernst, Sabine Eur Heart J State of the Art Review The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care. Oxford University Press 2021-08-15 /pmc/articles/PMC8497074/ /pubmed/34392353 http://dx.doi.org/10.1093/eurheartj/ehab544 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | State of the Art Review Nagarajan, Venkat D Lee, Su-Lin Robertus, Jan-Lukas Nienaber, Christoph A Trayanova, Natalia A Ernst, Sabine Artificial intelligence in the diagnosis and management of arrhythmias |
title | Artificial intelligence in the diagnosis and management of arrhythmias |
title_full | Artificial intelligence in the diagnosis and management of arrhythmias |
title_fullStr | Artificial intelligence in the diagnosis and management of arrhythmias |
title_full_unstemmed | Artificial intelligence in the diagnosis and management of arrhythmias |
title_short | Artificial intelligence in the diagnosis and management of arrhythmias |
title_sort | artificial intelligence in the diagnosis and management of arrhythmias |
topic | State of the Art Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497074/ https://www.ncbi.nlm.nih.gov/pubmed/34392353 http://dx.doi.org/10.1093/eurheartj/ehab544 |
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