GW28-e1219 Radiofrequency catheter ablation for paroxysmal atrial fibrillation: over 3-year follow-up outcome

Wang, Yubing; Xu, Yang; Ling, Zhiyu; Chen, Weijie; Li, Su; Du, Huaan; Xiao, Ping; Liu, Zengzhang; Yin, Yuehui

doi:10.1016/j.jacc.2017.07.453

Cited by 3 publications

(3 citation statements)

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“…However, the procedure is much less successful when dealing with persistent AF, which has a reoccurrence rate of ∼75% post-intervention. Therefore, with the high reoccurrence rate of AF, there is a need for improvements within ( Wang et al, 2017 ; Yubing et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation

Ogbomo-Harmitt

Muffoletto²,

Zeidan³

et al. 2023

Front. Physiol.

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Background: Radiofrequency catheter ablation (RFCA) therapy is the first-line treatment for atrial fibrillation (AF), the most common type of cardiac arrhythmia globally. However, the procedure currently has low success rates in dealing with persistent AF, with a reoccurrence rate of ∼50% post-ablation. Therefore, deep learning (DL) has increasingly been applied to improve RFCA treatment for AF. However, for a clinician to trust the prediction of a DL model, its decision process needs to be interpretable and have biomedical relevance.Aim: This study explores interpretability in DL prediction of successful RFCA therapy for AF and evaluates if pro-arrhythmogenic regions in the left atrium (LA) were used in its decision process.Methods: AF and its termination by RFCA have been simulated in MRI-derived 2D LA tissue models with segmented fibrotic regions (n = 187). Three ablation strategies were applied for each LA model: pulmonary vein isolation (PVI), fibrosis-based ablation (FIBRO) and a rotor-based ablation (ROTOR). The DL model was trained to predict the success of each RFCA strategy for each LA model. Three feature attribution (FA) map methods were then used to investigate interpretability of the DL model: GradCAM, Occlusions and LIME.Results: The developed DL model had an AUC (area under the receiver operating characteristic curve) of 0.78 ± 0.04 for predicting the success of the PVI strategy, 0.92 ± 0.02 for FIBRO and 0.77 ± 0.02 for ROTOR. GradCAM had the highest percentage of informative regions in the FA maps (62% for FIBRO and 71% for ROTOR) that coincided with the successful RFCA lesions known from the 2D LA simulations, but unseen by the DL model. Moreover, GradCAM had the smallest coincidence of informative regions of the FA maps with non-arrhythmogenic regions (25% for FIBRO and 27% for ROTOR).Conclusion: The most informative regions of the FA maps coincided with pro-arrhythmogenic regions, suggesting that the DL model leveraged structural features of MRI images to identify such regions and make its prediction. In the future, this technique could provide a clinician with a trustworthy decision support tool.

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Section: Introductionmentioning

confidence: 99%

Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation

Ogbomo-Harmitt

Muffoletto²,

Zeidan³

et al. 2023

Front. Physiol.

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“…RFCA procedures have a relatively high success rate in patients with paroxysmal AF (about 70% for a single procedure) (Oketani et al, 2012 ). However, in persistent AF patients, the arrhythmia can recur after RFCA in ~75% of cases (Wang et al, 2017 ). Cryo-ablation has emerged as an alternative, arguably more convenient method based on delivery of low temperatures through a balloon catheter.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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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“…RFCA procedures have relatively high success rate in patients with paroxysmal AF (about 70% for a single procedure) [4]. However, in persistent AF patients, the arrhythmia can recur after RFCA in~75% of cases [5]. This warrants the development of novel, more efficient ablation strategies [6].…”

Section: Introductionmentioning

confidence: 99%

Development of a Deep Learning Method to Predict Optimal Ablation Patterns for Atrial Fibrillation

Muffoletto

Roy

et al. 2019

2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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GW28-e1219 Radiofrequency catheter ablation for paroxysmal atrial fibrillation: over 3-year follow-up outcome

Cited by 3 publications

References 0 publications

Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation

Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation

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

Development of a Deep Learning Method to Predict Optimal Ablation Patterns for Atrial Fibrillation

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