Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation
Atrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/13/9/1870 |
| _version_ | 1797579181943422976 |
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| author | Yiheng Lyu Mohammed Bennamoun Naeha Sharif Gregory Y. H. Lip Girish Dwivedi |
| author_facet | Yiheng Lyu Mohammed Bennamoun Naeha Sharif Gregory Y. H. Lip Girish Dwivedi |
| author_sort | Yiheng Lyu |
| collection | DOAJ |
| description | Atrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in the management of atrial fibrillation, as they not only provide anatomical context to evaluate structural alterations but also help in determining treatment strategies. However, interpreting these images requires significant human expertise. The potential of artificial intelligence in analyzing these images has been repeatedly suggested due to its ability to automate the process with precision comparable to human experts. This review summarizes the benefits of artificial intelligence in enhancing the clinical care of patients with atrial fibrillation through cardiovascular image analysis. It provides a detailed overview of the two most critical steps in image-guided AF management, namely, segmentation and classification. For segmentation, the state-of-the-art artificial intelligence methodologies and the factors influencing the segmentation performance are discussed. For classification, the applications of artificial intelligence in the diagnosis and prognosis of atrial fibrillation are provided. Finally, this review also scrutinizes the current challenges hindering the clinical applicability of these methods, with the aim of guiding future research toward more effective integration into clinical practice. |
| first_indexed | 2024-03-10T22:33:16Z |
| format | Article |
| id | doaj.art-aac57f1901ac4f4cb68a6a50a4825532 |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-10T22:33:16Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-aac57f1901ac4f4cb68a6a50a48255322023-11-19T11:37:25ZengMDPI AGLife2075-17292023-09-01139187010.3390/life13091870Artificial Intelligence in the Image-Guided Care of Atrial FibrillationYiheng Lyu0Mohammed Bennamoun1Naeha Sharif2Gregory Y. H. Lip3Girish Dwivedi4Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, AustraliaDepartment of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, AustraliaDepartment of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, AustraliaLiverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool L69 3BX, UKHarry Perkins Institute of Medical Research, The University of Western Australia, Perth, WA 6009, AustraliaAtrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in the management of atrial fibrillation, as they not only provide anatomical context to evaluate structural alterations but also help in determining treatment strategies. However, interpreting these images requires significant human expertise. The potential of artificial intelligence in analyzing these images has been repeatedly suggested due to its ability to automate the process with precision comparable to human experts. This review summarizes the benefits of artificial intelligence in enhancing the clinical care of patients with atrial fibrillation through cardiovascular image analysis. It provides a detailed overview of the two most critical steps in image-guided AF management, namely, segmentation and classification. For segmentation, the state-of-the-art artificial intelligence methodologies and the factors influencing the segmentation performance are discussed. For classification, the applications of artificial intelligence in the diagnosis and prognosis of atrial fibrillation are provided. Finally, this review also scrutinizes the current challenges hindering the clinical applicability of these methods, with the aim of guiding future research toward more effective integration into clinical practice.https://www.mdpi.com/2075-1729/13/9/1870atrial fibrillationartificial intelligencemachine learningdeep learningechocardiographycomputed tomography |
| spellingShingle | Yiheng Lyu Mohammed Bennamoun Naeha Sharif Gregory Y. H. Lip Girish Dwivedi Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation Life atrial fibrillation artificial intelligence machine learning deep learning echocardiography computed tomography |
| title | Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation |
| title_full | Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation |
| title_fullStr | Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation |
| title_full_unstemmed | Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation |
| title_short | Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation |
| title_sort | artificial intelligence in the image guided care of atrial fibrillation |
| topic | atrial fibrillation artificial intelligence machine learning deep learning echocardiography computed tomography |
| url | https://www.mdpi.com/2075-1729/13/9/1870 |
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