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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

805
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
805

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

Eliot Crespin1, Arnaud Rosier1,2, Issam Ibnouhsein1

  • 1Implicity SAS, Paris, France.

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
|January 3, 2024
PubMed
Summary
This summary is machine-generated.

An artificial intelligence (AI) tool significantly reduced false positive diagnoses from insertable cardiac monitors (ICM). This AI-based ILR-ECG Analyzer™ preserves high sensitivity, easing caregiver workload.

Keywords:
arrhythmiaartificial intelligenceimplantable loop recorderinsertable cardiac monitormachine learningremote monitoring

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Area of Science:

  • Cardiology
  • Medical Technology
  • Artificial Intelligence

Background:

  • Insertable cardiac monitors (ICM) are increasingly used for cardiac monitoring.
  • High rates of false positive (FP) diagnoses from ICMs create significant workload for healthcare providers.
  • Efficient verification of ICM data is crucial for patient care.

Purpose of the Study:

  • To evaluate the performance of an AI-based ILR-ECG Analyzer™ (ILR-ECG-A) in reclassifying ICM events.
  • To minimize false positive diagnoses from ICMs while maintaining high sensitivity.
  • To assess the potential of AI to reduce the clinical burden associated with ICM data review.

Main Methods:

  • A cohort of 546 patients with ICMs monitored via the Implicity™ platform was analyzed.
  • One abnormal episode per patient was selected and analyzed by the ILR-ECG-A.
  • Episodes were compared against an adjudication committee (AC) review for accuracy.

Main Results:

  • Out of 879 abnormal ICM episodes, 32.2% were false positives (FP).
  • The ILR-ECG-A reclassified 76.0% of FP episodes as normal.
  • The AI algorithm confirmed 98.6% of truly abnormal episodes, demonstrating high sensitivity.

Conclusions:

  • The AI-based ILR-ECG-A significantly reduces false positive diagnoses from ICMs.
  • The tool maintains high sensitivity (>98%) for detecting true abnormalities.
  • Implementation of this AI solution is expected to substantially alleviate the clinical workload for ICM event review.