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

Eliot Crespin¹, Arnaud Rosier^1,2, Issam Ibnouhsein¹, Alexandre Gozlan¹, Arnaud Lazarus³, Gabriel Laurent⁴, Aymeric Menet⁵, Jean-Luc Bonnet¹, Niraj Varma⁶

¹Implicity SAS, Paris, France.

²Jacques Cartier Private Hospital, Massy, France.

³Service de rythmologie interventionnelle, Clinique Ambroise Paré, Neuilly sur Seine, France.

⁴Service de rythmologie et Insuffisance Cardiaque, Centre Hospitalier Universitaire, Dijon, France.

⁵Département de Cardiologie, Groupe Hospitalier de l'Institut Catholique de Lille, Lomme, France.

⁶Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, OH, USA.

PubMed PMC DOI PDF

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 preservi…

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