ArticleCardiovascular digital health journal2022
Artificial intelligence augments detection accuracy of cardiac insertable cardiac monitors: Results from a pilot prospective observational study.
Article in Cardiovascular digital health journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
LESTONNAC Study: Observed ST Elevation Does Not Require Acute Cardiac Necrosis
Evaluation of Electrocardiographic Data From High-risk Cardiac Patients Using Willem™ Cardiologist-level Artificial Intelligence Software. WILLEM Trial.
Who cites it
14 citing papers in PubMed.
- From wrist to heart: clinical validation and real-world translation of wearable and portable AI-enhanced electrocardiography.European heart journal. Digital health · 2026Review
- 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
- Real-world use of insertable cardiac monitor remote programming: A multicenter European experience.Heart rhythm O2 · 2025Article
- The hope and the hype of artificial intelligence for syncope management.European heart journal. Digital health · 2025Article
- Review
- Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor.Communications medicine · 2025Article
- Revolutionizing electrocardiography: the role of artificial intelligence in modern cardiac diagnostics.Annals of medicine and surgery (2012) · 2025Review
- Artificial Intelligence Algorithms in Cardiovascular Medicine: An Attainable Promise to Improve Patient Outcomes or an Inaccessible Investment?Current cardiology reports · 2024Review
- Review
- Monitoring of Remotely Reprogrammable Implantable Loop Recorders With Algorithms to Reduce False-Positive Alerts.Journal of the American Heart Association · 2024Article
- Implantable cardiac monitors: artificial intelligence and signal processing reduce remote ECG review workload and preserve arrhythmia detection sensitivity.Frontiers in cardiovascular medicine · 2024Article
- Current and Future Use of Artificial Intelligence in Electrocardiography.Journal of cardiovascular development and disease · 2023Review
- Searching for atrial fibrillation post stroke: is it time for digital devices?Frontiers in cardiovascular medicine · 2023Review
- Artificial intelligence: Revolutionizing pediatric emergency care - A narrative review.International journal of critical illness and injury scienceReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Insertable cardiac monitors (ICMs) are indicated for long-term monitoring of patients with unexplained syncope or who are at risk for cardiac arrhythmias. The volume of ICM-transmitted information may result in long data review times to identify true and clinically relevant arrhythmias. Objective: The purpose of this study was to evaluate whether artificial intelligence (AI) may improve ICM detection accuracy. Methods: We performed a retrospective analysis of consecutive patients implanted with the Confirm Rx Results: During mean follow-up of 23 months, of 20 ICM patients (mean age 68 ± 12 years; 50% women), 19 had 2261 SECGs recordings associated with cardiac arrhythmia detections or patient symptoms. True arrhythmias occurred in 11 patients: asystoles in 2, bradycardias in 3, ventricular tachycardias in 4, and atrial tachyarrhythmias (atrial tachycardia/atrial fibrillation [AT/AF]) in 10; with 6 patients having >1 arrhythmia type. AI algorithm overall accuracy for arrhythmia classification was 95.4%, with 97.19% sensitivity, 94.52% specificity, 89.74% positive predictive value, and 98.55% negative predictive value. Application of AI would have reduced the number of false-positive results by 98.0% overall: 94.0% for AT/AF, 87.5% for ventricular tachycardia, 99.5% for bradycardia, and 98.8% for asystole. Conclusion: Application of AI to ICM-detected episodes is associated with high classification accuracy and may significantly reduce health care staff workload by triaging ICM data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.