{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T06:31:40Z","timestamp":1772692300534,"version":"3.50.1"},"reference-count":106,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T00:00:00Z","timestamp":1693872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/life13091870","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T09:59:31Z","timestamp":1693907971000},"page":"1870","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7589-5599","authenticated-orcid":false,"given":"Yiheng","family":"Lyu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia"},{"name":"Harry Perkins Institute of Medical Research, The University of Western Australia, Perth, WA 6009, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6603-3257","authenticated-orcid":false,"given":"Mohammed","family":"Bennamoun","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8351-6288","authenticated-orcid":false,"given":"Naeha","family":"Sharif","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7566-1626","authenticated-orcid":false,"given":"Gregory Y. H.","family":"Lip","sequence":"additional","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool L69 3BX, UK"},{"name":"Liverpool John Moores University, Liverpool L3 5UX, UK"},{"name":"Liverpool Heart and Chest Hospital, Liverpool L14 3PE, UK"},{"name":"Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, 9220 Aalborg, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0717-740X","authenticated-orcid":false,"given":"Girish","family":"Dwivedi","sequence":"additional","affiliation":[{"name":"Harry Perkins Institute of Medical Research, The University of Western Australia, Perth, WA 6009, Australia"},{"name":"Department of Cardiology, Fiona Stanley Hospital, Perth, WA 6150, Australia"},{"name":"Medical School, The University of Western Australia, Perth, WA 6009, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1161\/CIRCULATIONAHA.113.005119","article-title":"Worldwide Epidemiology of Atrial Fibrillation","volume":"129","author":"Chugh","year":"2014","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1161\/CIRCRESAHA.117.309732","article-title":"Atrial Fibrillation","volume":"120","author":"Staerk","year":"2017","journal-title":"Circ. 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