ArticleFrontiers in cardiovascular medicine2022
Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 32 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Designing Chinese hospital emergency departments to leverage artificial intelligence-a systematic literature review on the challenges and opportunities.Frontiers in medical technology · 2024Pooled it
- An AIoT enabled system for optimizing data retrieval in the intensive care unit evaluated in a randomized crossover pilot trial.Scientific reports · 2025Trial
- Artificial intelligence-based ECG as a triage tool for acute myocardial infarction: a diagnostic systematic review and meta-analysis.European heart journal. Digital health · 2026Review
- Source-Only Transportability of Engineered ECG Features for Healthy-Versus-Myocardial Infarction Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence in prehospital assessment of acute coronary syndrome: a scoping review.BMC emergency medicine · 2026Article
- Artificial intelligence in cardiology in the current era: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Artificial intelligence-enhanced electrocardiography for acute myocardial infarction detection: a systematic review.Cardiovascular diagnosis and therapy · 2026Review
- Artificial Intelligence-Enhanced Telerehabilitation in Post-Acute Coronary Syndrome: A Narrative Review of Opportunities, Evidence, and Future Directions.Life (Basel, Switzerland) · 2026Review
- Acute coronary occlusion among cancelled STEMI alerts: a prospective study in a telemedicine-guided network.European heart journal. Digital health · 2026Article
- AI Applications in Electrocardiography for Ischemic and Structural Heart Disease: A Review of the Current State.Journal of clinical medicine · 2026Review
- Use of artificial intelligence in the out-of-hospital care settings: a scoping review.BMJ digital health & AI · 2026Article
- Artificial Intelligence in Cardiopulmonary Resuscitation.Medicina (Kaunas, Lithuania) · 2025Review
- The Accuracy of Artificial Intelligence-Based Models Applied to 12-Lead Electrocardiograms for the Diagnosis of Acute Coronary Syndrome: A Systematic Review.Journal of the American College of Emergency Physicians open · 2025Review
- Artificial intelligence for electrocardiographic diagnosis of perioperative myocardial ischaemia: a scoping review.British journal of anaesthesia · 2025Article
- The Lancet Commission on rethinking coronary artery disease: moving from ischaemia to atheroma.Lancet (London, England) · 2025Review
- Deep learning for electrocardiogram interpretation: Bench to bedside.European journal of clinical investigation · 2025Review
- Artificial Intelligence and Its Role in the Diagnosis and Prediction of Adverse Events in Acute Coronary Syndrome: A Narrative Review of the Literature.Life (Basel, Switzerland) · 2025Review
- Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease.Frontiers in artificial intelligence · 2025Article
- Evaluation of AI-enhanced tele-ECG response time and diagnosis in acute chest pain patients.Frontiers in cardiovascular medicine · 2025Article
- Prehospital ECG Interpretation Methods for ST-Elevation MI Detection and Catheterization Laboratory Activation: A Systematic Review and Meta-Analysis.Archives of academic emergency medicine · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
13 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To implement an all-day online artificial intelligence (AI)-assisted detection of ST-elevation myocardial infarction (STEMI) by prehospital 12-lead electrocardiograms (ECGs) to facilitate patient triage for timely reperfusion therapy. Methods: The proposed AI model combines a convolutional neural network and long short-term memory (CNN-LSTM) to predict STEMI on prehospital 12-lead ECGs obtained from mini-12-lead ECG devices equipped in ambulance vehicles in Central Taiwan. Emergency medical technicians (EMTs) from the 14 AI-implemented fire stations performed the on-site 12-lead ECG examinations using the mini portable device. The 12-lead ECG signals were transmitted to the AI center of China Medical University Hospital to classify the recordings as "STEMI" or "Not STEMI". In 11 non-AI fire stations, the ECG data were transmitted to a secure network and read by available on-line emergency physicians. The response time was defined as the time interval between the ECG transmission and ECG interpretation feedback. Results: Between July 17, 2021, and March 26, 2022, the AI model classified 362 prehospital 12-lead ECGs obtained from 275 consecutive patients who had called the 119 dispatch centers of fire stations in Central Taiwan for symptoms of chest pain or shortness of breath. The AI's response time to the EMTs in ambulance vehicles was 37.2 ± 11.3 s, which was shorter than the online physicians' response time from 11 other fire stations with no AI implementation (113.2 ± 369.4 s, Conclusion: Implementation of an all-day real-time AI-assisted remote detection of STEMI on prehospital 12-lead ECGs in the field is feasible with a high diagnostic accuracy rate. This approach may help minimize preventable delays in contact-to-treatment times for STEMI patients who require PPCI.
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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.