{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T13:42:52Z","timestamp":1782135772547,"version":"3.54.5"},"reference-count":33,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["JRSM Cardiovascular Disease"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:p>\n                    Atrial fibrillation (AF) is a clinically heterogeneous syndrome where traditional \u2018one-size-fits-all\u2019 management strategies are often suboptimal. This review synthesizes the contemporary application of machine learning (ML) and deep learning (DL) in identifying distinct clinical AF phenotypes to advance personalized treatment. We provide a comprehensive overview of over a dozen key phenotyping studies, highlighting the consistent identification of core patient subgroups across diverse international cohorts, including low-risk\/younger, atherosclerotic\/high-cardiovascular-risk, and elderly\/multi-morbid phenotypes. A detailed comparative summary of these studies, their methodologies, and their prognostic findings is presented. Our review also illustrates how these data-driven phenotypes are being leveraged to guide personalized therapy. We detail specific ML applications in optimizing medication selection and dosing, particularly for anticoagulants, and in advancing catheter ablation strategies. Key innovations in ablation include AI-guided anatomical mapping,\n                    <jats:italic toggle=\"yes\">in silico<\/jats:italic>\n                    simulation with \u2018cardiac digital twins\u2019 to test lesion sets pre-procedurally, and the identification of non-invasive predictors for procedural success. Finally, we discuss how phenotyping informs tailored lifestyle and risk factor management. While ML-driven phenotyping demonstrates powerful prognostic value, challenges in prospective validation, clinical integration, and model interpretability remain. This review concludes that a phenotype-guided approach holds transformative potential to move AF management towards a new era of precision medicine, improving outcomes by tailoring interventions to an individual's unique clinical profile.\n                  <\/jats:p>","DOI":"10.1177\/20480040251399275","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T17:27:22Z","timestamp":1763746042000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Clinical phenotypes of atrial fibrillation: A review of machine learning applications in personalized treatment"],"prefix":"10.1177","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4094-1222","authenticated-orcid":false,"given":"Richard J","family":"Woodman","sequence":"first","affiliation":[{"name":"Flinders University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8381-5253","authenticated-orcid":false,"given":"Huah Shin","family":"Ng","sequence":"additional","affiliation":[{"name":"Flinders University"},{"name":"Northern Adelaide Local Health Network"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8699-1412","authenticated-orcid":false,"given":"Arduino A","family":"Mangoni","sequence":"additional","affiliation":[{"name":"Flinders University"},{"name":"Southern Adelaide Local Health Network"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Odutayo A Wong CX Hsiao AJ et al. 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Quantifying a spectrum of clinical response in atrial tachyarrhythmias using spatiotemporal synchronization of electrograms. 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