Clinical phenotypes of atrial fibrillation: A review of machine learning applications in personalized treatment

Abstract

Atrial fibrillation (AF) is a clinically heterogeneous syndrome where traditional 'one-size-fits-all' 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, <i>in silico</i> simulation with 'cardiac digital twins' 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.

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