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

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.

Methods and results

We 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 dia

SUBMITTER: Crespin E 

PROVIDER: S-EPMC10787483 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature