Predicting Atrial Fibrillation Mechanisms Through Deep Learning

心房颤动（AF）是最常见的心律失常，仅在英国就有超过110万人受到影响，并与其他心血管疾病、中风和死亡的风险增加有关。对药物治疗无反应的患者可采用射频导管消融治疗，该治疗用于分离导致房颤的病理组织区域。房颤患者需要不同的治疗量：一些患者需要多次手术，采用更广泛的消融策略；而对其他人来说，使用射频导管消融对肺静脉进行更简单的隔离就足够了。预测消融治疗方法对特定患者是否足够是一个临床挑战，如果解决了这个问题，可以提高消融手术的安全性，并减少这些手术的时间和成本。针对患者特性进行个性化的计算生物物理模拟，包括心脏成像和电数据，可能为房颤和如何治疗提供实质性的见解，但运行速度太慢，无法在临床过程中使用。我的目标是开发一种结合生物物理模拟和机器学习的网络管道，准确量化单个患者消融治疗成功的可能性，以便在临床过程中使用，以指导消融治疗。机器学习网络将接受大量生物物理模拟数据的训练，以确保它正确捕获系统的物理和生理。然后，培训将随着临床数据的复杂性和现实性而增强。最后，深度学习管道将在回顾性研究中进行测试。我们希望这项研究将为这一预测管道提供概念证明。我们的新方法有可能彻底改变房颤预测建模领域，通过构建一个管道，使患者特异性治疗方法能够在单次消融过程中开发和应用。我们希望在未来，临床和研究中心将能够使用训练有素的机器学习网络来预测单个患者心房颤动的因素和不同消融手术的结果。这可能会提高消融手术的安全性，更好地选择患者，并减少这些手术的时间和成本。

Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting over 1.1 million people in the UK alone, and is associated with increased risk of other cardiovascular disease, stroke and death. Patients who do not respond to drug treatment may be treated using radio frequency catheter ablation therapy, which is used to isolate the areas of pathological tissue responsible for AF. AF patients require different amounts of treatment: some patients require multiple procedures, with more extensive ablation strategies; while for others, a more simple isolation of the pulmonary veins using radio frequency catheter ablation is sufficient. Predicting whether an ablation treatment approach is a sufficient treatment for a particular patient is a clinical challenge, which if solved could improve safety of ablation procedures, and decrease time and cost for these procedures.Computational biophysical simulations personalised to patient properties, including cardiac imaging and electrical data, may offer substantial insights into AF and how to treat it, but run too slowly to be used during clinical procedures. My objective is to develop a combined biophysical simulation and machine-learning network pipeline that accurately quantifies the likelihood of success of ablation therapies for an individual patient quickly enough for use during a clinical procedure, to guide ablation therapy. The machine-learning network will be trained to large quantities of biophysical simulated data to ensure that it correctly captures the physics and physiology of the system. The training will then be augmented with the complexity and reality of clinical data. Finally, the deep learning pipeline will be tested in a retrospective study.We hope that this study will provide a proof of concept for this predictive pipeline. Our novel approach has the potential to revolutionise the field of predictive modelling for AF by constructing a pipeline that enables patient-specific treatment approaches to be developed and applied during a single ablation procedure. We hope that in the future, clinical and research centres will be able to use the trained machine learning network to predict the factors responsible for AF in an individual patient and the outcome of different ablation procedures. This may lead to improved safety of ablation procedures, better patient selection, as well as decreased time and cost for these procedures.