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Toward Patient-Specific Prediction of Ablation Strategies for Atrial Fibrillation Using Deep Learning

Research output: Contribution to journalArticlepeer-review

Marica Muffoletto, Ahmed Qureshi, Aya Zeidan, Laila Muizniece, Xiao Fu, Jichao Zhao, Aditi Roy, Paul A. Bates, Oleg Aslanidi

Original languageEnglish
Article number674106
JournalFrontiers in Physiology
Volume12
Issue number674106
DOIs
Published26 May 2021

Bibliographical note

Funding Information: This work was supported by grants from the British Heart Foundation [PG/15/8/31138] (OA), the Engineering and Physical Sciences Research Council [EP/L015226/1] (MM and AR) and the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z] (OA), all held at King’s College London. XF and PB are funded and supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK, the UK Medical Research Council and the Wellcome Trust. This research was funded in whole, or in part, by the Wellcome Trust (FC001003). For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. Publisher Copyright: © Copyright © 2021 Muffoletto, Qureshi, Zeidan, Muizniece, Fu, Zhao, Roy, Bates and Aslanidi. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

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    fphys_12_674106.pdf, 3.18 MB, application/pdf

    Uploaded date:31 May 2021

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    Licence:CC BY

King's Authors

Abstract

Atrial fibrillation (AF) is a common cardiac arrhythmia that affects 1% of the population worldwide and is associated with high levels of morbidity and mortality. Catheter ablation (CA) has become one of the first line treatments for AF, but its success rates are suboptimal, especially in the case of persistent AF. Computational approaches have shown promise in predicting the CA strategy using simulations of atrial models, as well as applying deep learning to atrial images. We propose a novel approach that combines image-based computational modelling of the atria with deep learning classifiers trained on patient-specific atrial models, which can be used to assist in CA therapy selection. Therefore, we trained a deep convolutional neural network (CNN) using a combination of (i) 122 atrial tissue images obtained by unfolding patient LGE-MRI datasets, (ii) 157 additional synthetic images derived from the patient data to enhance the training dataset, and (iii) the outcomes of 558 CA simulations to terminate several AF scenarios in the corresponding image-based atrial models. Four CNN classifiers were trained on this patient-specific dataset balanced using several techniques to predict three common CA strategies from the patient atrial images: pulmonary vein isolation (PVI), rotor-based ablation (Rotor) and fibrosis-based ablation (Fibro). The training accuracy for these classifiers ranged from 96.22 to 97.69%, while the validation accuracy was from 78.68 to 86.50%. After training, the classifiers were applied to predict CA strategies for an unseen holdout test set of atrial images, and the results were compared to outcomes of the respective image-based simulations. The highest success rate was observed in the correct prediction of the Rotor and Fibro strategies (100%), whereas the PVI class was predicted in 33.33% of the cases. In conclusion, this study provides a proof-of-concept that deep neural networks can learn from patient-specific MRI datasets and image-derived models of AF, providing a novel technology to assist in tailoring CA therapy to a patient.

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