ArticleFrontiers in cardiovascular medicine2023
Machine learning analysis of complex late gadolinium enhancement patterns to improve risk prediction of major arrhythmic events.
Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 9 citations in OpenAlex.
- Late Gadolinium Enhancement Entropy as a Novel Imaging Biomarker of Myocardial Tissue Heterogeneity-A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2026Review
- Improving arrhythmic risk prediction using cardiac magnetic resonance within deep learning in ischemic heart disease.NPJ cardiovascular health · 2026Article
- Phase entropy of gated SPECT-MPI for predicting major adverse cardiovascular events: incremental prognostic value beyond perfusion and function.Frontiers in cardiovascular medicine · 2026Article
- Fibrosis Entropy Is Associated With Life-Threatening Arrhythmia in Nonischemic Cardiomyopathy.Journal of the American Heart Association · 2025Observational
- AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review.Journal of cardiovascular development and disease · 2025Review
- State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology.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 · 2025Article
- Prediction of sudden cardiac death using artificial intelligence: Current status and future directions.Heart rhythm · 2025Review
- Post-mortem Cardiac MRI in Sudden Cardiac Death: The Interesting Intertwining of Radiology and Histology to Diagnose Arrhythmic Death or Myocardial Infarction.Current medical imaging · 2025Article
- Implantable Cardioverter Defibrillator Tachycardia Therapies: Past, Present and Future Directions.Journal of cardiovascular development and disease · 2024Review
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
Background: Machine learning analysis of complex myocardial scar patterns affords the potential to enhance risk prediction of life-threatening arrhythmia in stable coronary artery disease (CAD). Objective: To assess the utility of computational image analysis, alongside a machine learning (ML) approach, to identify scar microstructure features on late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) that predict major arrhythmic events in patients with CAD. Methods: Patients with stable CAD were prospectively recruited into a CMR registry. Shape-based scar microstructure features characterizing heterogeneous ('peri-infarct') and homogeneous ('core') fibrosis were extracted. An ensemble of machine learning approaches were used for risk stratification, in addition to conventional analysis using Cox modeling. Results: Of 397 patients (mean LVEF 45.4 ± 16.0) followed for a median of 6 years, 55 patients (14%) experienced a major arrhythmic event. When applied within an ML model for binary classification, peri-infarct zone (PIZ) entropy, peri-infarct components and core interface area outperformed a model representative of the current standard of care (LVEF<35% and NYHA>Class I): AUROC (95%CI) 0.81 (0.81-0.82) vs. 0.64 (0.63-0.65), Conclusion: Machine learning models using LGE-CMR scar microstructure improved arrhythmic risk stratification as compared to guideline-based clinical parameters; highlighting a potential novel approach to identifying candidates for implantable cardioverter defibrillators in stable CAD.
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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.