ArticleComputers in biology and medicine2020
A hybrid machine learning approach to localizing the origin of ventricular tachycardia using 12-lead electrocardiograms.
Article in Computers in biology and medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Artificial Intelligence Enabled Noninvasive Mapping of Cardiac Arrhythmia Origins Using the 12-Lead Electrocardiogram.Journal of cardiovascular electrophysiology · 2026Review
- A joint CNN-Bi-LSTM-transformer architecture with SHAP explanations for multi-label arrhythmia detection from 12-lead ECGs.Scientific reports · 2026Article
- Artificial Intelligence in Cardiac Electrophysiology: A Comprehensive Review.Journal of personalized medicine · 2025Review
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 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
- Targeting Ventricular Arrhythmias in Non-Ischemic Patients: Advances in Diagnosis and Treatment.Diagnostics (Basel, Switzerland) · 2025Review
- Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke: development and validation of a hybrid machine learning model.Stroke and vascular neurology · 2024Article
- BOATMAP: Bayesian Optimization Active Targeting for Monomorphic Arrhythmia Pace-mapping.Computers in biology and medicine · 2024Article
- A machine learning approach to differentiate wide QRS tachycardia: distinguishing ventricular tachycardia from supraventricular tachycardia.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2024Article
- Ventricular Tachycardia Catheter Ablation: Retrospective Analysis and Prospective Outlooks-A Comprehensive Review.Biomedicines · 2024Review
- Emerging role of artificial intelligence in cardiac electrophysiology.Cardiovascular digital health journal · 2022Review
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis.International journal of arrhythmia · 2022Review
- Cardiac Activation Maps Reconstruction: A Comparative Study Between Data-Driven and Physics-Based Methods.Frontiers in physiology · 2021Article
- AI in the EP lab - mapping, imaging, and signal interpretation.Indian pacing and electrophysiology journalReview
- A hybrid random forest model for stroke risk prediction.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundMachine learning models may help localize the site of origin of ventricular tachycardia (VT) using 12-lead electrocardiograms. However, population-based models suffer from inter-subject anatomical variations within ECG data, while patient-specific models face the open challenge of what pacing data to collect for training.
methodsThis study presents and validates the first hybrid model that combines population and patient-specific machine learning for rapid "computer-guided pace-mapping". A population-based deep learning model was first trained offline to disentangle inter-subject variations and regionalize the site of VT origin. Given a new patient with a target VT, an on-line patient-specific model -- after being initialized by the population-based prediction -- was then built in real time by actively suggesting where to pace next and improving the prediction with each added pacing data, progressively guiding pace-mapping towards the site of VT origin.
resultsThe population model was trained on pace-mapping data from 38 patients and the patient-specific model was subsequently tuned on one patient. The resulting hybrid model was tested on a separate cohort of eight patients in localizing 1) 193 LV endocardial pacing sites, and 2) nine VTs with clinically determined exit sites. The hybrid model achieved a localization error of 5.3 ± 2.6 mm using 5.4 ± 2.5 pacing sites in localizing LV pacing sites, achieving a significantly higher accuracy with a significantly smaller amount of training sites in comparison to models without active guidance.
conclusionThe presented hybrid model has the potential to assist rapid pace-mapping of interventional targets in VT.
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