In-Procedure Personalized Atrial Digital Twin to Predict Outcome of Atrial Fibrillation Ablation

房颤是一种心脏节律紊乱，主要影响老年人，增加中风的风险，降低生活质量。房颤可以通过射频消融来治疗，射频消融会不可逆转地破坏引发和维持心律失常的组织区域。目前，消融的成功与否只能在治疗完成后才能评估。心脏数字双胞胎提供了患者的物理表示，并随着更多信息的获得而更新。这些双胞胎可以用来预测患者对房颤消融的反应。我们建议使用正在接受房颤消融手术的患者的数字双胞胎，通过术前成像和重要的术中详细的侵入性测量来预测心房对消融的反应。这将为创造数字双胞胎提供基础，这些数字双胞胎可以用于指导和优化针对个别患者的房颤消融治疗。我们和其他人已经为个别患者创建了心脏模型，但个性化过程是时间密集型的，因为这些模型具有许多自由度。因此，这个项目的主要挑战是加速个性化和预测，以便它们可以在临床过程中可用的30-45分钟时间窗口内实现。我们将通过(1)优化使用在过程前可以收集的关于单个患者的信息以及来自以前患者群体的信息来解决这一挑战；(2)识别关于个人的最具信息量的模型参数，并且可以从临床测量中识别；(3)选择有效的方法来加速计算模拟；以及(4)对用于快速过程内个性化的新方法进行系统研究，包括最先进的机器学习方法。将从头到尾采取概率方法，预测将取决于数据和模型拟合的质量。然后，我们的工作流程将被集成，以产生一个易于使用的软件平台，该平台适合在临床环境中部署，用于20名患者的概念验证临床研究。该项目将开发技术，生成试点数据，创建业务案例，并与患者接触，以便我们能够进行单独和广泛的临床研究，以确定我们工作流程的有效性及其是否适合常规临床使用。

Atrial fibrillation (AF) is a disorder of heart rhythm that predominantly affects older people, increasing the risk of stroke and reducing quality of life. AF can be treated by radio-frequency ablation, which irreversibly destroys regions of tissue that initiate and sustain the arrhythmia. Currently, success of the ablation can only be evaluated after the therapy has been delivered. Cardiac digital twins provide a physics-based representation of the patient that is updated as more information becomes available. These twins can be used to predict how a patient will respond to an AF ablation. We propose to use digital twins of patients undergoing an AF ablation procedure, created from both pre-procedure imaging, and importantly, detailed in-procedure invasive measurements, to predict how the atria will respond to ablation. This will provide the basis for creating digital twins that can be used to guide and optimise AF ablation therapies for individual patients. Heart models for individual patients have been created by us and others, but the personalisation process is time intensive because the models have many degrees of freedom. The main challenge for this project is therefore to accelerate both personalisation and prediction so that they can be achieved within the 30-45 minute time window that is available during a clinical procedure.We will address this challenge by (1) optimising the use of information about an individual patient that can be gathered prior to the procedure, as well as information from a population of previous patients; (2) identifying model parameters that are most informative about an individual and which can be identified from clinical measurements; (3) selecting effective methods to accelerate computational simulations; and (4) a systematic investigation of novel approaches for rapid in-procedure personalisation, including state-of-the-art machine learning methods. A probabilistic approach will be taken throughout, and predictions will depend on the quality of the data and model fits. Our workflow will then be integrated to produce an easy to use software platform that is suitable for deployment in the clinical setting for a proof-of-concept clinical study in 20 patients. The project will develop the technology, generate pilot data, create a business case, and engage with patients so that we can undertake a separate and extensive clinical study to establish the effectiveness of our workflow and its suitability for routine clinical use.