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Arrhythmogenic propensity of the fibrotic substrate after atrial fibrillation ablation: a longitudinal study using magnetic resonance imaging-based atrial models

Cardiovascular research, 2019-10, Vol.115 (12), p.1757-1765

2019

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Feasibility of using patient-specific models and the “minimum cut” algorithm to predict optimal ablation targets for left atrial flutter

Heart rhythm, 2016-08, Vol.13 (8), p.1687-1698

2016

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Elevated fibrosis burden as assessed by MRI predicts cryoballoon ablation failure

Journal of cardiovascular electrophysiology, 2023-02, Vol.34 (2), p.302-312

2023

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Explainable Machine Learning to Predict Anchored Reentry Substrate Created by Persistent Atrial Fibrillation Ablation in Computational Models

Journal of the American Heart Association, 2023-08, Vol.12 (16), p.e030500-e030500

2023

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MRI-quantified left atrial epicardial adipose tissue predicts atrial fibrillation recurrence following catheter ablation

Frontiers in cardiovascular medicine, 2022-12, Vol.9, p.1045742-1045742

2022

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Arrhythmia dynamics in computational models of the atria following virtual ablation of re-entrant drivers

Europace (London, England), 2018-11, Vol.20 (suppl_3), p.iii45-iii54

2018

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Dantrolene Improves Survival After Ventricular Fibrillation by Mitigating Impaired Calcium Handling in Animal Models

Circulation (New York, N.Y.), 2014-02, Vol.129 (8), p.875-885

2014

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Fibrosis, atrial fibrillation and stroke: clinical updates and emerging mechanistic models

Heart (British Cardiac Society), 2021-01, Vol.107 (2), p.99-105

2021

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Fusion during entrainment of orthodromic reciprocating tachycardia is enhanced for basal pacing sites but diminished when pacing near Purkinje system end points

Heart rhythm, 2013-03, Vol.10 (3), p.444-451

2013

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Machine Learning and the Conundrum of Stroke Risk Prediction

Arrhythmia & electrophysiology review, 2023-01, Vol.12, p.e07

2023

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Patient-derived models link re-entrant driver localization in atrial fibrillation to fibrosis spatial pattern

Cardiovascular research, 2016-06, Vol.110 (3), p.443-454

2016

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Epicardial adipose tissue is associated with left atrial volume and fibrosis in patients with atrial fibrillation

Frontiers in cardiovascular medicine, 2022-11, Vol.9, p.1045730-1045730

2022

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Building a machine learning-assisted echocardiography prediction tool for children at risk for cancer therapy-related cardiomyopathy

Cardio-oncology (London, England), 2024-10, Vol.10 (1), p.66-11, Article 66

2024

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Translational applications of computational modelling for patients with cardiac arrhythmias

Heart (British Cardiac Society), 2021-03, Vol.107 (6), p.456-461

2021

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Calcium Dynamics and Cardiac Arrhythmia

Clinical Medicine Insights. Cardiology, 2017, Vol.2017, p.1-4-015

2017

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Navigating the Stormy Sea of Anisotropy: How Electroanatomic Properties Influence Complex Propagation in Atrial Fibrillation

JACC. Clinical electrophysiology, 2024-07, Vol.10 (7), p.1605-1607

2024

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Towards personalized computational modelling of the fibrotic substrate for atrial arrhythmia

Europace (London, England), 2016-12, Vol.18 (suppl 4), p.iv136-iv145

2016

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Modelling methodology of atrial fibrosis affects rotor dynamics and electrograms

Europace (London, England), 2016-12, Vol.18 (suppl 4), p.iv146-iv155

2016

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Computational Medicine: What Electrophysiologists Should Know to Stay Ahead of the Curve

Current cardiology reports, 2024-09

2024

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Machine learning identifies esophageal luminal temperature patterns associated with thermal injury in catheter ablation for atrial fibrillation

Journal of cardiovascular electrophysiology, 2024-04, Vol.35 (4), p.737-746

2024

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Prediction of Shock-Refractory Ventricular Fibrillation During Resuscitation of Out-of-Hospital Cardiac Arrest

Circulation (New York, N.Y.), 2023-07, Vol.148 (4), p.327-335

2023

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Leave the light on: chronic optogenetic tachypacing of human engineered cardiac tissue constructs

Cardiovascular research, 2020-07, Vol.116 (8), p.1405-1406

2020

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Ventricular arrhythmia risk prediction in repaired Tetralogy of Fallot using personalized computational cardiac models

Heart rhythm, 2020-03, Vol.17 (3), p.408-414

2020

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Assessment of arrhythmia mechanism and burden of the infarcted ventricles following remuscularization with pluripotent stem cell-derived cardiomyocyte patches using patient-derived models

Cardiovascular research, 2022-03, Vol.118 (5), p.1247-1261

2022

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Light-based Approaches to Cardiac Arrhythmia Research: From Basic Science to Translational Applications

Clinical Medicine Insights. Cardiology, 2016-01, Vol.2016 (S1), p.47-60

2016

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Arrhythmogenic propensity of the fibrotic substrate after atrial fibrillation ablation: a longitudinal study using magnetic resonance imaging-based atrial models

Feasibility of using patient-specific models and the “minimum cut” algorithm to predict optimal ablation targets for left atrial flutter

Elevated fibrosis burden as assessed by MRI predicts cryoballoon ablation failure

Explainable Machine Learning to Predict Anchored Reentry Substrate Created by Persistent Atrial Fibrillation Ablation in Computational Models

MRI-quantified left atrial epicardial adipose tissue predicts atrial fibrillation recurrence following catheter ablation

Arrhythmia dynamics in computational models of the atria following virtual ablation of re-entrant drivers

Dantrolene Improves Survival After Ventricular Fibrillation by Mitigating Impaired Calcium Handling in Animal Models

Fibrosis, atrial fibrillation and stroke: clinical updates and emerging mechanistic models

Fusion during entrainment of orthodromic reciprocating tachycardia is enhanced for basal pacing sites but diminished when pacing near Purkinje system end points

Machine Learning and the Conundrum of Stroke Risk Prediction

Patient-derived models link re-entrant driver localization in atrial fibrillation to fibrosis spatial pattern

Epicardial adipose tissue is associated with left atrial volume and fibrosis in patients with atrial fibrillation

Building a machine learning-assisted echocardiography prediction tool for children at risk for cancer therapy-related cardiomyopathy

Translational applications of computational modelling for patients with cardiac arrhythmias

Calcium Dynamics and Cardiac Arrhythmia

Navigating the Stormy Sea of Anisotropy: How Electroanatomic Properties Influence Complex Propagation in Atrial Fibrillation

Towards personalized computational modelling of the fibrotic substrate for atrial arrhythmia

Modelling methodology of atrial fibrosis affects rotor dynamics and electrograms

Computational Medicine: What Electrophysiologists Should Know to Stay Ahead of the Curve

Machine learning identifies esophageal luminal temperature patterns associated with thermal injury in catheter ablation for atrial fibrillation

Prediction of Shock-Refractory Ventricular Fibrillation During Resuscitation of Out-of-Hospital Cardiac Arrest

Leave the light on: chronic optogenetic tachypacing of human engineered cardiac tissue constructs

Ventricular arrhythmia risk prediction in repaired Tetralogy of Fallot using personalized computational cardiac models

Assessment of arrhythmia mechanism and burden of the infarcted ventricles following remuscularization with pluripotent stem cell-derived cardiomyocyte patches using patient-derived models

Light-based Approaches to Cardiac Arrhythmia Research: From Basic Science to Translational Applications

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