Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation.
Journal:
American journal of physiology. Heart and circulatory physiology
PMID:
33513086
Abstract
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 patients with AF.
Authors
Keywords
Action Potentials
Artificial Intelligence
Atrial Fibrillation
Clinical Decision-Making
Diagnosis, Computer-Assisted
Heart Conduction System
Heart Function Tests
Heart Rate
Humans
Machine Learning
Neural Networks, Computer
Pattern Recognition, Automated
Predictive Value of Tests
Therapy, Computer-Assisted