Caroline Roney

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Lecturer in Computational Medicine

Queen Mary University of London

United Kingdom

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Predicting Atrial Fibrillation Mechanisms Through Deep Learning

Open Date: 2018-09-30

Close Date: 2021-09-29

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Collaborators (11)

Dhani Dharmaprani

Flinders University

AUSTRALIA

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Elizabeth Cherry

Georgia Institute of Technology

UNITED STATES

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Jennifer Tweedy

University of Bath

UNITED KINGDOM

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Yingjing Feng

University of Birmingham

UNITED KINGDOM

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Fadi G. Akar

Associate Professor

Yale University

UNITED STATES

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Richard Wilkinson

Professor of Statistics

University of Nottingham

UNITED KINGDOM

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Anand N Ganesan

AUSTRALIA

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Tina Baykaner

UNITED STATES

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Orod Razeghi

University of Cambridge

UNITED KINGDOM

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GONZALO RICARDO RIOS MUÑOZ

SPAIN

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Richard Clayton

Professor of Computational Physiology

University of Sheffield

UNITED KINGDOM

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Caroline Roney

Research Interests

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Recent Grants

Articles (19)

A Semi-automatic Pipeline for Generation of Large Cohorts of Four-Chamber Heart Meshes

High Prevalence of New Clinically Significant Findings in Patients With Embolic Stroke of Unknown Source Evaluated by Cardiac Magnetic Resonance Imaging

Quantifying the impact of shape uncertainty on predicted arrhythmias

Evaluation of an open-source pipeline to create patient-specific left atrial models: A reproducibility study

Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography

Constructing bilayer and volumetric atrial models at scale

AF and in-hospital mortality in COVID-19 patients

Deep neural network architectures for cardiac image segmentation

Fiber Organization Has Little Effect on Electrical Activation Patterns During Focal Arrhythmias in the Left Atrium

In Atrial Fibrillation, Omnipolar Voltage Maps More Accurately Delineate Scar Than Bipolar Voltage Maps

Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds

Detection of focal source and arrhythmogenic substrate from body surface potentials to guide atrial fibrillation ablation

The inspection paradox: An important consideration in the evaluation of rotor lifetimes in cardiac fibrillation.

Applications of multimodality imaging for left atrial catheter ablation

Time-Averaged Wavefront Analysis Demonstrates Preferential Pathways of Atrial Fibrillation, Predicting Pulmonary Vein Isolation Acute Response

Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate

Using Machine Learning to Identify Local Cellular properties that Support Re- entrant Activation in Patient Specific Models of Atrial Fibrillation

Anisotropic cardiac conduction

Left Atrial Enhancement Correlates With Myocardial Conduction Velocity in Patients With Persistent Atrial Fibrillation

Collaborators (11)

Dhani Dharmaprani

Elizabeth Cherry

Jennifer Tweedy

Yingjing Feng

Fadi G. Akar

Richard Wilkinson

Anand N Ganesan

Tina Baykaner

Orod Razeghi

GONZALO RICARDO RIOS MUÑOZ

Richard Clayton

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