Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis
The integration of artificial intelligence (AI) into clinical management of aortic stenosis (AS) has redefined our approach to the assessment and management of this heterogenous valvular heart disease (VHD). While the large-scale early detection of valvular conditions is limited by socioeconomic con...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IMR Press
2024-01-01
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| Series: | Reviews in Cardiovascular Medicine |
| Subjects: | |
| Online Access: | https://www.imrpress.com/journal/RCM/25/1/10.31083/j.rcm2501031 |
| _version_ | 1797338502018367488 |
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| author | Yuxuan Zhang Moyang Wang Erli Zhang Yongjian Wu |
| author_facet | Yuxuan Zhang Moyang Wang Erli Zhang Yongjian Wu |
| author_sort | Yuxuan Zhang |
| collection | DOAJ |
| description | The integration of artificial intelligence (AI) into clinical management of aortic stenosis (AS) has redefined our approach to the assessment and management of this heterogenous valvular heart disease (VHD). While the large-scale early detection of valvular conditions is limited by socioeconomic constraints, AI offers a cost-effective alternative solution for screening by utilizing conventional tools, including electrocardiograms and community-level auscultations, thereby facilitating early detection, prevention, and treatment of AS. Furthermore, AI sheds light on the varied nature of AS, once considered a uniform condition, allowing for more nuanced, data-driven risk assessments and treatment plans. This presents an opportunity to re-evaluate the complexity of AS and to refine treatment using data-driven risk stratification beyond traditional guidelines. AI can be used to support treatment decisions including device selection, procedural techniques, and follow-up surveillance of transcatheter aortic valve replacement (TAVR) in a reproducible manner. While recognizing notable AI achievements, it is important to remember that AI applications in AS still require collaboration with human expertise due to potential limitations such as its susceptibility to bias, and the critical nature of healthcare. This synergy underpins our optimistic view of AI’s promising role in the AS clinical pathway. |
| first_indexed | 2024-03-08T09:32:10Z |
| format | Article |
| id | doaj.art-b0a709b3cda64713b3e855e529eea193 |
| institution | Directory Open Access Journal |
| issn | 1530-6550 |
| language | English |
| last_indexed | 2024-03-08T09:32:10Z |
| publishDate | 2024-01-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Reviews in Cardiovascular Medicine |
| spelling | doaj.art-b0a709b3cda64713b3e855e529eea1932024-01-31T01:12:56ZengIMR PressReviews in Cardiovascular Medicine1530-65502024-01-012513110.31083/j.rcm2501031S1530-6550(23)01142-0Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic StenosisYuxuan Zhang0Moyang Wang1Erli Zhang2Yongjian Wu3Department of Cardiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, ChinaDepartment of Cardiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, ChinaDepartment of Cardiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, ChinaDepartment of Cardiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, ChinaThe integration of artificial intelligence (AI) into clinical management of aortic stenosis (AS) has redefined our approach to the assessment and management of this heterogenous valvular heart disease (VHD). While the large-scale early detection of valvular conditions is limited by socioeconomic constraints, AI offers a cost-effective alternative solution for screening by utilizing conventional tools, including electrocardiograms and community-level auscultations, thereby facilitating early detection, prevention, and treatment of AS. Furthermore, AI sheds light on the varied nature of AS, once considered a uniform condition, allowing for more nuanced, data-driven risk assessments and treatment plans. This presents an opportunity to re-evaluate the complexity of AS and to refine treatment using data-driven risk stratification beyond traditional guidelines. AI can be used to support treatment decisions including device selection, procedural techniques, and follow-up surveillance of transcatheter aortic valve replacement (TAVR) in a reproducible manner. While recognizing notable AI achievements, it is important to remember that AI applications in AS still require collaboration with human expertise due to potential limitations such as its susceptibility to bias, and the critical nature of healthcare. This synergy underpins our optimistic view of AI’s promising role in the AS clinical pathway.https://www.imrpress.com/journal/RCM/25/1/10.31083/j.rcm2501031aortic stenosisartificial intelligencescreeningrisk stratificationtavrsurveillance |
| spellingShingle | Yuxuan Zhang Moyang Wang Erli Zhang Yongjian Wu Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis Reviews in Cardiovascular Medicine aortic stenosis artificial intelligence screening risk stratification tavr surveillance |
| title | Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis |
| title_full | Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis |
| title_fullStr | Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis |
| title_full_unstemmed | Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis |
| title_short | Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis |
| title_sort | artificial intelligence in the screening diagnosis and management of aortic stenosis |
| topic | aortic stenosis artificial intelligence screening risk stratification tavr surveillance |
| url | https://www.imrpress.com/journal/RCM/25/1/10.31083/j.rcm2501031 |
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