Artificial intelligence for the detection, prediction, and management of atrial fibrillation
Jonas L. Isaksen, Mathias Baumert, Astrid N L Hermans, Molly Maleckar, Dominik Linz
Abstract
The present article reviews the state of the art of machine learning algorithms for the detection, prediction, and management of atrial fibrillation (AF), as well as of the development and evaluation of artificial intelligence (AI) in cardiology and beyond. Today, AI detects AF with a high accuracy using 12-lead or single-lead electrocardiograms or photoplethysmography. The prediction of paroxysmal or future AF currently operates at a level of precision that is too low for clinical use. Further studies are needed to determine whether patient selection for interventions may be possible with machine learning.
Topics & Concepts
Atrial fibrillationArtificial intelligenceComputer scienceMachine learningParoxysmal atrial fibrillationManagement of atrial fibrillationCardiologyInternal medicineMedicineAtrial Fibrillation Management and OutcomesECG Monitoring and AnalysisCardiac Arrhythmias and Treatments