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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/3/689 |
| _version_ | 1827649363982155776 |
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| author | Yu-Chiang Wang Xiaobo Xu Adrija Hajra Samuel Apple Amrin Kharawala Gustavo Duarte Wasla Liaqat Yiwen Fu Weijia Li Yiyun Chen Robert T. Faillace |
| author_facet | Yu-Chiang Wang Xiaobo Xu Adrija Hajra Samuel Apple Amrin Kharawala Gustavo Duarte Wasla Liaqat Yiwen Fu Weijia Li Yiyun Chen Robert T. Faillace |
| author_sort | Yu-Chiang Wang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T19:56:23Z |
| format | Article |
| id | doaj.art-28c4d51804494e6487e5222f6b836ec5 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T19:56:23Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-28c4d51804494e6487e5222f6b836ec52023-11-24T00:55:45ZengMDPI AGDiagnostics2075-44182022-03-0112368910.3390/diagnostics12030689Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review StudyYu-Chiang Wang0Xiaobo Xu1Adrija Hajra2Samuel Apple3Amrin Kharawala4Gustavo Duarte5Wasla Liaqat6Yiwen Fu7Weijia Li8Yiyun Chen9Robert T. Faillace10Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USADepartment of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USADepartment of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USADepartment of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USADepartment of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USADepartment of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USADepartment of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USADepartment of Medicine, Kaiser Permanente Santa Clara Medical Center, Santa Clara, CA 95051, USADepartment of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USADepartment of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USADepartment of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USAAtrial 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.https://www.mdpi.com/2075-4418/12/3/689atrial fibrillationartificial intelligencewearable devicesmachine learning |
| spellingShingle | Yu-Chiang Wang Xiaobo Xu Adrija Hajra Samuel Apple Amrin Kharawala Gustavo Duarte Wasla Liaqat Yiwen Fu Weijia Li Yiyun Chen Robert T. Faillace Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study Diagnostics atrial fibrillation artificial intelligence wearable devices machine learning |
| 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 | atrial fibrillation artificial intelligence wearable devices machine learning |
| url | https://www.mdpi.com/2075-4418/12/3/689 |
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