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Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study

Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyo...

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Autores principales: Wang, Yu-Chiang, Xu, Xiaobo, Hajra, Adrija, Apple, Samuel, Kharawala, Amrin, Duarte, Gustavo, Liaqat, Wasla, Fu, Yiwen, Li, Weijia, Chen, Yiyun, Faillace, Robert T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947563/
https://www.ncbi.nlm.nih.gov/pubmed/35328243
http://dx.doi.org/10.3390/diagnostics12030689
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author Wang, Yu-Chiang
Xu, Xiaobo
Hajra, Adrija
Apple, Samuel
Kharawala, Amrin
Duarte, Gustavo
Liaqat, Wasla
Fu, Yiwen
Li, Weijia
Chen, Yiyun
Faillace, Robert T.
author_facet Wang, Yu-Chiang
Xu, Xiaobo
Hajra, Adrija
Apple, Samuel
Kharawala, Amrin
Duarte, Gustavo
Liaqat, Wasla
Fu, Yiwen
Li, Weijia
Chen, Yiyun
Faillace, Robert T.
author_sort Wang, Yu-Chiang
collection PubMed
description Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future.
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spelling pubmed-89475632022-03-25 Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study Wang, Yu-Chiang Xu, Xiaobo Hajra, Adrija Apple, Samuel Kharawala, Amrin Duarte, Gustavo Liaqat, Wasla Fu, Yiwen Li, Weijia Chen, Yiyun Faillace, Robert T. Diagnostics (Basel) Review Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future. MDPI 2022-03-11 /pmc/articles/PMC8947563/ /pubmed/35328243 http://dx.doi.org/10.3390/diagnostics12030689 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Wang, Yu-Chiang
Xu, Xiaobo
Hajra, Adrija
Apple, Samuel
Kharawala, Amrin
Duarte, Gustavo
Liaqat, Wasla
Fu, Yiwen
Li, Weijia
Chen, Yiyun
Faillace, Robert T.
Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study
title Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study
title_full Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study
title_fullStr Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study
title_full_unstemmed Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study
title_short Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study
title_sort current advancement in diagnosing atrial fibrillation by utilizing wearable devices and artificial intelligence: a review study
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947563/
https://www.ncbi.nlm.nih.gov/pubmed/35328243
http://dx.doi.org/10.3390/diagnostics12030689
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