Esther Puyol Anton

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King’s College London

United Kingdom

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Automating analysis and interpretation of Cardiac MR using Quality-Controlled AI

Open Date: 2020-08-01

Close Date: 2020-12-01

Articles (13)

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2025

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2020

Collaborators (11)

Chris Finan

University College London

UNITED KINGDOM

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Reza Razavi

King’s College London

UNITED KINGDOM

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Stefan K. Piechnik

Head of Advaned Cardiovascular Image Analysis, OCMR

University of Oxford

UNITED KINGDOM

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Sander W. van der Laan

Assistant Professor

University Medical Center Groningen

NETHERLANDS

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Andrew King

King’s College London

UNITED KINGDOM

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John Whitaker

King's College London

UNITED KINGDOM

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ye li

King’s College London

UNITED KINGDOM

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Folkert Wouter Asselbergs

University College London

UNITED KINGDOM

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Chen Chen

University of Sheffield

UNITED KINGDOM

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Abdulrahman Alasiri

University Medical Center Utrecht, Utrecht University

NETHERLANDS

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Alistair Young

King’s College London

UNITED KINGDOM

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Esther Puyol Anton

Research Interests

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Recent Grants

Articles (13)

DeepSPV: A deep learning pipeline for 3D spleen volume estimation from 2D ultrasound images

An investigation into the causes of race bias in artificial intelligence–based cine cardiac magnetic resonance segmentation

A Deep Learning-Based Integrated Framework for Quality-Aware Undersampled Cine Cardiac MRI Reconstruction and Analysis

Druggable proteins influencing cardiac structure and function: Implications for heart failure therapies and cancer cardiotoxicity

Hemodynamic Determinants of Elevated Blood Pressure and Hypertension in the Middle to Older-Age UK Population: A UK Biobank Imaging Study

Uncertainty aware training to improve deep learning model calibration for classification of cardiac MR images

Non-invasive localization of post-infarct ventricular tachycardia exit sites to guide ablation planning: a computational deep learning platform utilizing the 12-lead electrocardiogram and intracardiac electrograms from implanted devices

A multimodal deep learning model for cardiac resynchronisation therapy response prediction

Fairness in Cardiac Magnetic Resonance Imaging

Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank

Prevalence and Disease Expression of Pathogenic and Likely Pathogenic Variants Associated With Inherited Cardiomyopathies in the General Population

Digital Bank Runs: A Deep Neural Network Approach

Automated quantification of myocardial tissue characteristics from native T<inf>1</inf>mapping using neural networks with uncertainty-based quality-control

Collaborators (11)

Chris Finan

Reza Razavi

Stefan K. Piechnik

Sander W. van der Laan

Andrew King

John Whitaker

ye li

Folkert Wouter Asselbergs

Chen Chen

Abdulrahman Alasiri

Alistair Young

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