ArticleScientific reports2024
AI-enabled ECG index for predicting left ventricular dysfunction in patients with ST-segment elevation myocardial infarction.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- From Automated ECG Interpretation to Multimodal Cardiovascular Intelligence: The Evolution of Artificial Intelligence in Cardiovascular Medicine.Medical sciences (Basel, Switzerland) · 2026Review
- Source-Only Transportability of Engineered ECG Features for Healthy-Versus-Myocardial Infarction Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Silent Myocardial Infarction Revisited: Immuno-metabolic Mechanisms, Multimodal Biomarkers, and Translational Diagnostics.Journal of cardiovascular translational research · 2026Review
- A Narrative Review of Risk Assessment Approaches for Implantable Defibrillator Therapy to Prevent Sudden Cardiac Death in Acute Myocardial Infarction.Cardiology research and practice · 2026Review
- Machine Learning-Based Prediction of Early Left Ventricular Function After STEMI.Journal of clinical medicine · 2025Article
- Indirect Myocardial Injury in Polytrauma: Mechanistic Pathways and the Clinical Utility of Immunological Markers.Journal of cardiovascular development and disease · 2025Review
- Design and validation of a novel multiple sites signal acquisition and analysis system based on pressure stimulation for human cardiovascular information.Scientific reports · 2025Article
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Authors and funding
16 authors.
Funding
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Abstract
Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients are associated with prognosis. This study investigated the feasibility of predicting left ventricular (LV) dysfunction in STEMI patients using an artificial intelligence (AI)-enabled ECG algorithm developed to diagnose STEMI. Serial ECGs from 637 STEMI patients were analyzed with the AI algorithm, which quantified the probability of STEMI at various time points. The time points included pre-PCI, immediately post-PCI, 6 h post-PCI, 24 h post-PCI, at discharge, and one-month post-PCI. The prevalence of LV dysfunction was significantly associated with the AI-derived probability index. A high probability index was an independent predictor of LV dysfunction, with higher cardiac death and heart failure hospitalization rates observed in patients with higher indices. The study demonstrates that the AI-enabled ECG index effectively quantifies ECG changes post-PCI and serves as a digital biomarker capable of predicting post-STEMI LV dysfunction, heart failure, and mortality. These findings suggest that AI-enabled ECG analysis can be a valuable tool in the early identification of high-risk patients, enabling timely and targeted interventions to improve clinical outcomes in STEMI patients.
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