In American journal of physiology. Heart and circulatory physiology
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.
Sánchez de la Nava Ana Maria, Atienza Felipe, Bermejo Javier, Fernández-Avilés Francisco
artificial intelligence, atrial fibrillation, neural network, supervised learning, unsupervised learning