Improved diagnostic performance of insertable cardiac monitors by an artificial intelligence-based algorithm.

Journal: Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
PMID: 38170474

Abstract

AIMS: The increasing use of insertable cardiac monitors (ICM) produces a high rate of false positive (FP) diagnoses. Their verification results in a high workload for caregivers. We evaluated the performance of an artificial intelligence (AI)-based ILR-ECG Analyzer™ (ILR-ECG-A). This machine-learning algorithm reclassifies ICM-transmitted events to minimize the rate of FP diagnoses, while preserving device sensitivity.

Authors

  • Eliot Crespin
    Implicity SAS, Paris, France.
  • Arnaud Rosier
  • Issam Ibnouhsein
    Quantmetry, Paris, France.
  • Alexandre Gozlan
    Implicity SAS, Paris, France.
  • Arnaud Lazarus
    Service de rythmologie interventionnelle, Clinique Ambroise Paré, Neuilly sur Seine, France.
  • Gabriel Laurent
    Service de rythmologie et Insuffisance Cardiaque, Centre Hospitalier Universitaire, Dijon, France.
  • Aymeric Menet
    Département de Cardiologie, Groupe Hospitalier de l'Institut Catholique de Lille, Lomme, France.
  • Jean-Luc Bonnet
    Implicity SAS, Paris, France.
  • Niraj Varma
    Department of Cardiovascular Medicine, Heart and Vascular Institute (J.R., D.P., S.T., K.M.T., N.V., M.J.N., E.Z.G., R.A.G., M.K.C.), Cleveland Clinic, OH.

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