Cargando…
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...
Autores principales: | , , , , , , , , , , |
---|---|
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 |
_version_ | 1784674469617336320 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8947563 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT wangyuchiang currentadvancementindiagnosingatrialfibrillationbyutilizingwearabledevicesandartificialintelligenceareviewstudy AT xuxiaobo currentadvancementindiagnosingatrialfibrillationbyutilizingwearabledevicesandartificialintelligenceareviewstudy AT hajraadrija currentadvancementindiagnosingatrialfibrillationbyutilizingwearabledevicesandartificialintelligenceareviewstudy AT applesamuel currentadvancementindiagnosingatrialfibrillationbyutilizingwearabledevicesandartificialintelligenceareviewstudy AT kharawalaamrin currentadvancementindiagnosingatrialfibrillationbyutilizingwearabledevicesandartificialintelligenceareviewstudy AT duartegustavo currentadvancementindiagnosingatrialfibrillationbyutilizingwearabledevicesandartificialintelligenceareviewstudy AT liaqatwasla currentadvancementindiagnosingatrialfibrillationbyutilizingwearabledevicesandartificialintelligenceareviewstudy AT fuyiwen currentadvancementindiagnosingatrialfibrillationbyutilizingwearabledevicesandartificialintelligenceareviewstudy AT liweijia currentadvancementindiagnosingatrialfibrillationbyutilizingwearabledevicesandartificialintelligenceareviewstudy AT chenyiyun currentadvancementindiagnosingatrialfibrillationbyutilizingwearabledevicesandartificialintelligenceareviewstudy AT faillacerobertt currentadvancementindiagnosingatrialfibrillationbyutilizingwearabledevicesandartificialintelligenceareviewstudy |