ArticleMedical image analysis2021
Inference of ventricular activation properties from non-invasive electrocardiography.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- I-GraphECG: Observability-Based Lead Selection and Interpretable Disease Prediction from the 12-Lead ECG Using a Gray-Box Graph Electrophysiology Surrogate.Sensors (Basel, Switzerland) · 2026Article
- Uncertainty quantification of conduction velocity in models of cardiac spread of activation.Medical & biological engineering & computing · 2026Article
- Toward cardiac electrophysiology digital twins with an efficient open source scalable solver on GPU clusters.Scientific reports · 2026Article
- 3D cardiac shape analysis with variational point cloud autoencoders for myocardial infarction prediction and virtual heart synthesis.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- Harnessing 12-lead ECG and MRI data to personalise repolarisation profiles in cardiac digital twin models for enhanced virtual drug testing.Medical image analysis · 2025Article
- Definitions and Characteristics of Patient Digital Twins Being Developed for Clinical Use: Scoping Review.Journal of medical Internet research · 2024Article
- Evolution of digital twins in precision health applications: a scoping review study.Research square · 2024Article
- Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference.IEEE transactions on medical imaging · 2024Article
- The beating heart: artificial intelligence for cardiovascular application in the clinic.Magma (New York, N.Y.) · 2024Review
- Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy.Frontiers in physiology · 2023Article
- Non-invasive monitoring of cardiac function through Ballistocardiogram: an algorithm integrating short-time Fourier transform and ensemble empirical mode decomposition.Frontiers in physiology · 2023Article
- Model-based estimation of AV-nodal refractory period and conduction delay trends from ECG.Frontiers in physiology · 2023Article
- The health digital twin to tackle cardiovascular disease-a review of an emerging interdisciplinary field.NPJ digital medicine · 2022Review
- An Integrated Workflow for Building Digital Twins of Cardiac Electromechanics-A Multi-Fidelity Approach for Personalising Active Mechanics.Mathematics (Basel, Switzerland) · 2022Article
- A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- Uncertainty Quantification of Regional Cardiac Tissue Properties in Arrhythmogenic Cardiomyopathy Using Adaptive Multiple Importance Sampling.Frontiers in physiology · 2021Article
- Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data.Frontiers in physiology · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients' cardiac function to inform therapeutic and diagnostic decision-making. Both electrocardiography and imaging are used for the clinical diagnosis of cardiac disease. The integration of multi-modal datasets through advanced computational methods could enable the development of the cardiac 'digital twin', a comprehensive virtual tool that mechanistically reveals a patient's heart condition from clinical data and simulates treatment outcomes. The adoption of cardiac digital twins requires the non-invasive efficient personalisation of the electrophysiological properties in cardiac models. This study develops new computational techniques to estimate key ventricular activation properties for individual subjects by exploiting the synergy between non-invasive electrocardiography, cardiac magnetic resonance (CMR) imaging and modelling and simulation. More precisely, we present an efficient sequential Monte Carlo approximate Bayesian computation-based inference method, integrated with Eikonal simulations and torso-biventricular models constructed based on clinical CMR imaging. The method also includes a novel strategy to treat combined continuous (conduction speeds) and discrete (earliest activation sites) parameter spaces and an efficient dynamic time warping-based ECG comparison algorithm. We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac ventricular myocardial-mass volumes ranging from 74 cm
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Registered trials
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