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

房颤（AF）是一种不规则的心律，影响英国约100万人。它增加了其他心血管疾病的风险，包括心力衰竭，中风和死亡。对药物治疗无反应的患者可以使用射频导管消融治疗，其目的是隔离导致AF的病理组织区域。在更晚期的AF患者中，治疗是次优的，40%的患者在18个月随访时患有AF复发。对于这些患者，可能需要2-3次重复手术。不同的临床中心和不同的临床医生使用不同的消融方法，并根据个体患者的解剖结构、电气特性和病史进行不同程度的个性化。在目前的技术水平中，有大量的临床人群数据集可供使用，这些数据集可为人群中的平均患者提供治疗方法。与此同时，也有越来越多的更详细的患者特定模型可用。然而，这些方法通常是不相交的。我的愿景是，我们将在人群中测量的信息与患者特定的预测治疗模型联系起来。根据患者群体收集的数据，为个体患者量身定制消融治疗，将改善长期疗效并减少复发，从而使患者需要更少和更短的手术时间。心脏电信号标测和成像系统提供了大量的空间和时间测量，用于表征患者群体中的心房。这些数据可用于构建心房的计算生物物理模型，这反过来又提供了一个生理和物理约束的框架，用于研究个性化患者特定模型中的AF属性。研究这些生物物理模型的大型虚拟患者队列可以为AF治疗方法提供重要见解。然而，这些计算机模型运行速度太慢，无法在临床程序中使用，目前只能捕捉治疗后立即发生的情况，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捉复杂的关系并生成快速预测。为了克服在临床时间范围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，以快速预测患者成像和电气数据的治疗结果。我还将使用成像数据集来显示心房结构在手术后几个月内的变化。使用这些测量值更新模型（或数字孪生）将提高模型或网络预测患者长期结果的能力。该项目将模型从研究环境转移到临床应用。这些方法将结合到一个临床工具中，该工具处理成像和电气测量，以在临床手术期间将群体治疗结果映射到患者特定的预测治疗。该工具将获取患者的成像和电气数据，并输出不同的消融治疗方法以及它们减少AF复发（并改善结局）的可能性，以帮助患者制定特定的治疗计划。该项目拥有临床和工业项目合作伙伴，以实现该技术的临床转化。使用本研究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.