ArticleEuropace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology2023
Improved diagnostic performance of insertable cardiac monitors by an artificial intelligence-based algorithm.
Article in 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, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 18 citations in OpenAlex.
- PASTEC: an open clinical infrastructure for data aggregation and prospective validation of AI in cardiac remote monitoring.NPJ digital medicine · 2026Article
- Economic and clinical impact of diagnostic sensitivity for the use of insertable cardiac monitors to detect atrial fibrillation in cryptogenic stroke patients in the United Kingdom.BMC cardiovascular disorders · 2026Article
- Safety, performance, and remote programming utilization of an insertable cardiac monitor: The LUX-Dx PERFORM study.Heart rhythm O2 · 2026Article
- Atrial fibrillation detection performance of an insertable cardiac monitor: Results from an Assert-IQ post-market clinical study and a novel artificial intelligence algorithm.Heart rhythm O2 · 2026Article
- EHRA perspective on the digital data revolution in arrhythmia management: insights from the association's annual summit.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 · 2025Review
- The present and future of cardiological telemonitoring in Europe: a statement from seven European countries.Herzschrittmachertherapie & Elektrophysiologie · 2025Review
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThe 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. METHODS AND
resultsWe selected 546 recipients of ICM followed by the Implicity™ monitoring platform. To avoid clusterization, a single episode per ICM abnormal diagnosis (e.g. asystole, bradycardia, atrial tachycardia (AT)/atrial fibrillation (AF), ventricular tachycardia, artefact) was selected per patient, and analyzed by the ILR-ECG-A, applying the same diagnoses as the ICM. All episodes were reviewed by an adjudication committee (AC) and the results were compared. Among 879 episodes classified as abnormal by the ICM, 80 (9.1%) were adjudicated as 'Artefacts', 283 (32.2%) as FP, and 516 (58.7%) as 'abnormal' by the AC. The algorithm reclassified 215 of the 283 FP as normal (76.0%), and confirmed 509 of the 516 episodes as abnormal (98.6%). Seven undiagnosed false negatives were adjudicated as AT or non-specific abnormality. The overall diagnostic specificity was 76.0% and the sensitivity was 98.6%.
conclusionThe new AI-based ILR-ECG-A lowered the rate of FP ICM diagnoses significantly while retaining a > 98% sensitivity. This will likely alleviate considerably the clinical burden represented by the review of ICM events.
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