ArticleMedical image analysis2019
A rule-based method for predicting the electrical activation of the heart with cardiac resynchronization therapy from non-invasive clinical data.
Article in Medical image analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Prediction of arrhythmogenesis in non-ischemic cardiac resynchronization therapy patients.APL bioengineering · 2026Article
- Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models.PLoS computational biology · 2026Article
- Weaving the Digital Tapestry: Methods for Emulating Cohorts of Cardiac Digital Twins Using Gaussian Processes.Annals of biomedical engineering · 2026Article
- Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization.Nature cardiovascular research · 2025Article
- Quest for the ideal assessment of electrical ventricular dyssynchrony in cardiac resynchronization therapy.Journal of molecular and cellular cardiology plus · 2024Review
- Article
- A systematic review of cardiacProgress in biomedical engineering (Bristol, England) · 2023Review
- Comparison of novel ventricular pacing strategies using an electro-mechanical simulation platform.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 · 2023Article
- Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators.PLoS computational biology · 2023Article
- Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy.Frontiers in physiology · 2023Article
- Credibility assessment of patient-specific computational modeling using patient-specific cardiac modeling as an exemplar.PLoS computational biology · 2022Article
- Computational electrophysiology of the coronary sinus branches based on electro-anatomical mapping for the prediction of the latest activated region.Medical & biological engineering & computing · 2022Article
- Determining anatomical and electrophysiological detail requirements for computational ventricular models of porcine myocardial infarction.Computers in biology and medicine · 2022Article
- GEASI: Geodesic-based earliest activation sites identification in cardiac models.International journal for numerical methods in biomedical engineering · 2021Article
- Linking statistical shape models and simulated function in the healthy adult human heart.PLoS computational biology · 2021Article
- Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data.Frontiers in physiology · 2021Article
- Calibration of ionic and cellular cardiac electrophysiology models.Wiley interdisciplinary reviews. Systems biology and medicine · 2020Review
- Creation and application of virtual patient cohorts of heart models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
- A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations.PloS one · 2020Article
- Sex-Dependent QRS Guidelines for Cardiac Resynchronization Therapy Using Computer Model Predictions.Biophysical journal · 2019Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundCardiac Resynchronization Therapy (CRT) is one of the few effective treatments for heart failure patients with ventricular dyssynchrony. The pacing location of the left ventricle is indicated as a determinant of CRT outcome.
objectivePatient specific computational models allow the activation pattern following CRT implant to be predicted and this may be used to optimize CRT lead placement.
methodsIn this study, the effects of heterogeneous cardiac substrate (scar, fast endocardial conduction, slow septal conduction, functional block) on accurately predicting the electrical activation of the LV epicardium were tested to determine the minimal detail required to create a rule based model of cardiac electrophysiology. Non-invasive clinical data (CT or CMR images and 12 lead ECG) from eighteen patients from two centers were used to investigate the models.
resultsValidation with invasive electro-anatomical mapping data identified that computer models with fast endocardial conduction were able to predict the electrical activation with a mean distance errors of 9.2 ± 0.5 mm (CMR data) or (CT data) 7.5 ± 0.7 mm.
conclusionThis study identified a simple rule-based fast endocardial conduction model, built using non-invasive clinical data that can be used to rapidly and robustly predict the electrical activation of the heart. Pre-procedural prediction of the latest electrically activating region to identify the optimal LV pacing site could potentially be a useful clinical planning tool for CRT procedures.
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