Mapping populations to patients: designing optimal ablation therapy for atrial fibrillation through simulation and deep learning of digital twins

房颤(房颤)是一种不规则的心律，在英国约有100万人受到影响。它会增加其他心血管疾病的风险，包括心力衰竭、中风和死亡。对药物治疗无效的患者可以采用射频导管消融治疗，其目的是隔离引起房颤的病理组织区域。在更严重的房颤患者中，治疗效果不佳，40%的患者在18个月的随访中出现房颤复发。对于这些患者，可能需要重复2-3次手术。不同的临床中心和不同的临床医生使用不同的消融方法，对个别患者的解剖、电气特性和病史进行不同程度的个性化。在目前的技术水平下，有大量的临床人群数据集可用来为人群中的普通患者提供治疗方法。与此同时，也有越来越详细的针对患者的模型可用。然而，这些方法通常是不相交的。我的愿景是，我们将整个人群中测量的信息与特定于患者的模型联系起来，以进行预测性治疗。使用从人群中收集的数据为个体患者量身定做消融治疗，将改善长期结果并减少复发，因此患者需要更少和更短的程序。心脏电信号映射和成像系统提供大量的空间和时间测量，以表征不同人群的患者的心房。这些数据可用于构建心房的计算生物物理模型，这反过来又为研究个性化患者特定模型中的房颤属性提供了一个生理和物理约束框架。研究这些生物物理模型的大量虚拟患者队列可以为房颤的治疗方法提供重要的见解。然而，这些计算机模型运行速度太慢，无法在临床过程中使用，目前只能捕捉治疗后立即发生的事情，而不能捕捉长期反应(例如，手术后一年)。机器学习技术可以捕获复杂的关系并生成快速预测。为了克服在临床时间框架内预测长期反应的挑战，我将训练一个机器学习网络到大型虚拟患者队列和临床数据集，以根据患者的成像和电子数据快速预测治疗结果。我还将使用成像数据集，显示在手术后的几个月中心房的结构是如何变化的。用这些测量更新模型(或数字孪生)将提高模型或网络预测患者长期结果的能力。这个项目将把模型从研究环境转移到临床应用。这些方法将被结合到一个临床工具中，该工具处理成像和电子测量，以在临床过程中将人群治疗结果映射到患者特定的预测性治疗。该工具将获取患者的影像和电学数据，并输出不同的消融治疗方法以及它们减少房颤复发(和改善结果)的可能性，以帮助制定针对患者的治疗计划。该项目有临床和工业项目合作伙伴，以实现技术的临床翻译。使用在这次联谊会期间开发的方法可能会导致更好的治疗选择，并减少房颤导管消融手术的时间和成本。

Atrial Fibrillation (AF) is an irregular heart rhythm that affects ~1 million people in the UK. It increases the risk of other cardiovascular diseases including heart failure, stroke and death. Patients who do not respond to drug therapy may be treated using radio frequency catheter ablation treatment, which aims to isolate areas of pathological tissue responsible for AF. In more advanced AF patients, treatment is sub-optimal with 40% of patients suffering from AF recurrence at 18-month follow-up. For these patients, 2-3 repeat procedures may be required. Different ablation approaches are used by different clinical centres and different clinicians, with varying degrees of personalisation to the individual patient anatomy, electrical properties and history. In the current state of the art, there are large clinical population datasets available that inform treatment approaches for the average patient within a population. In parallel to this, there are also increasingly more detailed patient-specific models available. However, these approaches are typically disjoint. My vision is that we link information measured across a population to patient-specific models for predictive treatments. Tailoring ablation therapy to the individual patient, using data collected across a population, will improve long-term outcome and reduce recurrence, so that patients require fewer and shorter procedures.Cardiac electrical signal mapping and imaging systems provide large quantities of spatial and temporal measurements for characterising the atria across populations of patients. These data can be used for constructing computational biophysical models of the atria, which in turn provide a physiological and physics constrained framework for investigating AF properties in personalised patient-specific models. Studying large virtual patient cohorts of these biophysical models can provide important insights into AF treatment approaches. However, these computer models run too slowly to be used during clinical procedures, and currently only capture what happens immediately after the treatment, and not the long-term response (for example, a year after the procedure). Machine learning techniques can capture complex relationships and generate fast predictions. To overcome the challenge of predicting the long-term response within a clinical timeframe, I will train a machine learning network to the large virtual patient cohort and clinical datasets to quickly predict treatment outcome from patient imaging and electrical data. I will also use imaging datasets that show how the structure of the atria changes during the months following the procedure. Updating the model (or digital twin) with these measurements will improve the ability of the model or network to predict the long-term outcome for the patient.This project will move models from the research environment to clinical applications. These methodologies will be combined into a clinical tool that processes imaging and electrical measurements to map population therapy outcomes to patient-specific predictive treatments during the clinical procedure. The tool will take imaging and electrical data for a patient, and output different ablation therapy approaches together with how likely they are to reduce AF recurrence (and improve outcome), to aid patient-specific treatment planning. This project has clinical and industrial project partners to enable clinical translation of the technology. Use of the methodology developed during this fellowship may lead to better treatment selection, and decreased time and cost for AF catheter ablation procedures.