Observational studyNature medicine2023
Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction.
Observational study in Nature medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 142 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
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Who cites it
142 citing papers in PubMed, 240 citations in OpenAlex.
- Diagnostic performance of the ECG-SMART AI score for detecting angiographically significant coronary artery disease in patients undergoing elective coronary angiography.PLOS digital health · 2026Article
- Occlusion myocardial infarction in type I NSTEMI: prevalence, characteristics, management, and long-term outcome.Clinical research in cardiology : official journal of the German Cardiac Society · 2026Article
- Improved Detection of Acute Coronary Occlusion Myocardial Infarction by an Artificial Intelligence Electrocardiogram Model in Swedish Emergency Departments.Journal of the American College of Emergency Physicians open · 2026Article
- Beyond Traditional Risk Scores: Artificial Intelligence in Coronary Plaque Characterization and Personalized Atherosclerosis Management.Journal of cardiovascular development and disease · 2026Review
- A high-resolution circulating metabolic atlas of lung adenocarcinoma progression supports early and accurate diagnosis.Cell reports. Medicine · 2026Article
- Cardiac parameter estimation from electrocardiograms based on inversion of physical simulator behavior.PLOS digital health · 2026Article
- A Wearable Infrared Sensor for Detecting Non-ST Segment Elevation Acute Coronary Syndromes.Life (Basel, Switzerland) · 2026Article
- Source-Only Transportability of Engineered ECG Features for Healthy-Versus-Myocardial Infarction Classification.Diagnostics (Basel, Switzerland) · 2026Article
- From Electrocardiography to the Catheterization Laboratory: A Multimodal Artificial Intelligence Framework for Acute Coronary Syndrome Detection and Risk Stratification.Diagnostics (Basel, Switzerland) · 2026Review
- Deep learning analysis of ECGs detects Cardiovascular-Kidney-Metabolic syndrome burden in people with diabetes: a report from the Silesia Diabetes-Heart Project.Cardiovascular diabetology · 2026Observational
- CaReS-BiNet: A multi-scale deep learning framework for ECG arrhythmia classification.Scientific reports · 2026Article
- Modeling day-long ECG signals to predict heart failure risk with explainable AI.NPJ digital medicine · 2026Article
- Large language models for acute coronary syndrome triage at first medical contact in emergency departments.NPJ digital medicine · 2026Article
- Machine learning models to predict the recurrence of common bile duct stones after ERCP.BMC gastroenterology · 2026Article
- Artificial intelligence in prehospital assessment of acute coronary syndrome: a scoping review.BMC emergency medicine · 2026Article
- Novel machine learning fusion architectures integrating electrocardiogram representations: applications to acute coronary event detection.European heart journal. Digital health · 2026Article
- The Rlign algorithm for enhanced electrocardiogram analysis through heart rate-corrected ECG alignment for explainable classification and clustering.European heart journal. Digital health · 2026Article
- Sex-Consistent Performance of an AI-Enabled ECG for Acute Myocardial Infarction: The ROMIAE Study.JACC. Advances · 2026Article
- A deep learning ECG model for identification and localization of occlusion myocardial infarction.Nature communications · 2026Article
- Acute Coronary Occlusion in NSTEMI Patients: Prevalence, Clinical Characteristics and the Potential Role of Artificial Intelligence.Medicina (Kaunas, Lithuania) · 2026Article
82 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
19 authors at 9 institutions in 4 countries.
Funding
Abstract
Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.
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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.