Subclinical Atrial Fibrillation: A Silent Threat with Uncertain Implications

Anthony H. Kashou, Demilade A. Adedinsewo, Peter A. Noseworthy

Research output: Contribution to journalReview articlepeer-review

Abstract

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes-termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence-enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF.

Original languageEnglish (US)
Pages (from-to)355-362
Number of pages8
JournalAnnual Review of Medicine
Volume73
DOIs
StatePublished - 2022

Keywords

  • Artificial intelligence
  • Atrial fibrillation
  • Convolutional neural network
  • Deep neural network
  • ECG
  • Electrocardiogram
  • Machine learning
  • Sinus rhythm

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology

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