Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation
Ana María Sánchez de la Nava, Felipe Atienza, Javier Bermejo, Francisco Fernández-Avilés
AI summary
100% confidenceMES research in membrane, wastewater. Although atrial fibrillation (AF) is the most common cardiac arrhythmia, its early identific...
Generated by MESSAI extraction pipeline · review against source PDF
Reported parameters
No values extracted from this paper yet.
No 3D model is mapped to this paper yet. Parameter ranges above still place reported values on the literature distribution.
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.
Keywords
Identifiers
- Journal
- American Journal of Physiology-Heart and Circulatory Physiology
- Year
- 2021