Although Atrial Fibrillation (AF) is the most common cardiac arrhythmia, its early identification, diagnosis, and treatment is still challenging. Due to its heterogeneous mechanisms and risk-factors, targeting an individualized treatment of AF demands a large amount of patient data to identify specific patterns. Artificial Intelligence (AI) algorithms are particularly well suited for treating high-dimensional data, predicting outcomes and, eventually, optimizing strategies for patient management. The analysis of large patient samples combining different sources of information such as blood biomarkers, electrical signals and medical images opens a new paradigm for improving diagnostic algorithms. In this review, we summarize suitable AI techniques for this purpose. In particular, we describe potential applications for understanding the structural and functional bases of the disease, as well as for improving early noninvasive diagnosis, developing more efficient therapies, and predicting long-term clinical outcomes of AF patients.

中文翻译：

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人工智能用于心房颤动的个性化诊断和治疗

尽管房颤（AF）是最常见的心律不齐，但其早期识别，诊断和治疗仍具有挑战性。由于其异构机制和风险因素，针对AF的个性化治疗需要大量的患者数据以识别特定的模式。人工智能（AI）算法特别适合处理高维数据，预测结果并最终优化患者管理策略。结合不同信息源（例如血液生物标志物，电信号和医学图像）的大型患者样本的分析为改善诊断算法开辟了新的范例。在这篇综述中，我们总结了适合此目的的AI技术。特别是，