Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation.

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  • Additional Information
    • Source:
      Publisher: American Physiological Society Country of Publication: United States NLM ID: 100901228 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1522-1539 (Electronic) Linking ISSN: 03636135 NLM ISO Abbreviation: Am J Physiol Heart Circ Physiol Subsets: MEDLINE
    • Publication Information:
      Original Publication: Bethesda, Md. : American Physiological Society,
    • Subject Terms:
      Artificial Intelligence* ; Diagnosis, Computer-Assisted* ; Heart Function Tests* ; Therapy, Computer-Assisted*; Atrial Fibrillation/*diagnosis ; Atrial Fibrillation/*therapy; Action Potentials ; Atrial Fibrillation/physiopathology ; Clinical Decision-Making ; Heart Conduction System/physiopathology ; Heart Rate ; Humans ; Machine Learning ; Neural Networks, Computer ; Pattern Recognition, Automated ; Predictive Value of Tests
    • 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.
    • Contributed Indexing:
      Keywords: artificial intelligence; atrial fibrillation; classification; neural network; prediction
    • Publication Date:
      Date Created: 20210129 Date Completed: 20210419 Latest Revision: 20210419
    • Publication Date:
      20221213
    • Accession Number:
      10.1152/ajpheart.00764.2020
    • Accession Number:
      33513086