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

Muffoletto, Marica; Fu, Xiao; Roy, Aditi; Varela, Marta; Bates, Paul A.; Aslanidi, Oleg

doi:10.1109/cibcb.2019.8791475

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…This work builds upon previous proof-of-concept results that deep neural networks can learn from computational simulations of atrial electrical activation, to identify CA strategies (Muffoletto et al, 2019). This earlier study was based on atrial simulations run on synthetic 2D tissues with simple, randomly assigned geometric structures representing fibrosis.…”

Section: Discussionmentioning

confidence: 94%

“…We consider three main CA strategies (PVI, rotor-based and fibrosis-based) and also two AF scenarios that represent early-and late-stage AF. The advancements on our previous work (Muffoletto et al, 2019) include: (i) the use of real patient images to generate atrial models and train the CNN, (ii) the use of a wider range of simulated AF scenarios and ablation strategies to produce more accurate labels for the images, (iii) optimisation of the CNN parameters to produce higher accuracy in training and validation, and (iv) more in-depth analysis of the CNN classification between the CA strategies.…”

Section: Discussionmentioning

confidence: 99%

“…We have previously developed a method for training CNNs on a set of synthetic data composed of 2D atrial tissues with randomly distributed fibrotic patches (Muffoletto et al, 2019 ). To prove its effectiveness and provide proof-of-concept results for patients, we will apply this approach to patient-specific 2D atria obtained by unfolding of 3D atrial datasets from AF patients.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…And this approach has been shown to be more accurate than these purely image-driven learning schemes for identifying ablation targets ( Lozoya et al, 2019 ). These findings have important consequences for clinical decision-making as they indicate how mechanistic and statistical models work together to determine ablation targets ( Ali et al, 2019 ; Muffoletto et al, 2019 ; Cámara-Vázquez et al, 2021 ; Gander et al, 2022 ).…”

Section: Applications Of Digital Twin Techniques In Atrial Fibrillati...mentioning

confidence: 99%

How synergy between mechanistic and statistical models is impacting research in atrial fibrillation

Bai

Wang

et al. 2022

Front. Physiol.

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Atrial fibrillation (AF) with multiple complications, high morbidity and mortality, and low cure rates, has become a global public health problem. Although significant progress has been made in the treatment methods represented by anti-AF drugs and radiofrequency ablation, the therapeutic effect is not as good as expected. The reason is mainly because of our lack of understanding of AF mechanisms. This field has benefited from mechanistic and (or) statistical methodologies. Recent renewed interest in digital twin techniques by synergizing between mechanistic and statistical models has opened new frontiers in AF analysis. In the review, we briefly present findings that gave rise to the AF pathophysiology and current therapeutic modalities. We then summarize the achievements of digital twin technologies in three aspects: understanding AF mechanisms, screening anti-AF drugs and optimizing ablation strategies. Finally, we discuss the challenges that hinder the clinical application of the digital twin heart. With the rapid progress in data reuse and sharing, we expect their application to realize the transition from AF description to response prediction.

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“…Machine learning (ML), as with most fields, has begun to see a considerable application to cardiac electrophysiology. These include automated extraction of subtle information from the electrogram (Yang et al, 2018;Mincholé et al, 2019) and the identification of promising targets or success rates for ablation (Zahid et al, 2016;Muffoletto et al, 2019Muffoletto et al, , 2021Shade et al, 2020). In this study, we generate a large number of different realisations of fibrotic arrangement corresponding to significantly damaged tissue and then apply a single stimulus originating from many different points.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Identification of Pro-arrhythmic Structures in Cardiac Fibrosis

et al. 2021

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Cardiac fibrosis and other scarring of the heart, arising from conditions ranging from myocardial infarction to ageing, promotes dangerous arrhythmias by blocking the healthy propagation of cardiac excitation. Owing to the complexity of the dynamics of electrical signalling in the heart, however, the connection between different arrangements of blockage and various arrhythmic consequences remains poorly understood. Where a mechanism defies traditional understanding, machine learning can be invaluable for enabling accurate prediction of quantities of interest (measures of arrhythmic risk) in terms of predictor variables (such as the arrangement or pattern of obstructive scarring). In this study, we simulate the propagation of the action potential (AP) in tissue affected by fibrotic changes and hence detect sites that initiate re-entrant activation patterns. By separately considering multiple different stimulus regimes, we directly observe and quantify the sensitivity of re-entry formation to activation sequence in the fibrotic region. Then, by extracting the fibrotic structures around locations that both do and do not initiate re-entries, we use neural networks to determine to what extent re-entry initiation is predictable, and over what spatial scale conduction heterogeneities appear to act to produce this effect. We find that structural information within about 0.5 mm of a given point is sufficient to predict structures that initiate re-entry with more than 90% accuracy.

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

Cited by 6 publications

References 18 publications

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

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

How synergy between mechanistic and statistical models is impacting research in atrial fibrillation

Machine Learning Identification of Pro-arrhythmic Structures in Cardiac Fibrosis

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