Rapid prediction of prosthetic heart valve haemodynamic performance using physics-informed machine learning

该项目旨在创建快速和可扩展的基于深度学习的模拟技术，用于预测经导管主动脉瓣(TAV)植入物的血流动力学性能。对于外科风险较高的患者，TAVs是治疗严重的主动脉瓣狭窄的有效方法。然而，各种临床和技术挑战阻碍了更广泛的患者接受该程序。计算建模工具可以在迄今未经过测试的场景(低风险组、双尖瓣解剖等)中实现TAV的性能。进行非侵入性和安全的评估。这样的能力支撑了正在兴起的硅内试验(ISTS)概念。血流和瓣膜的复杂相互作用需要使用流体-结构相互作用模型来进行血流动力学评估。FSI模型很昂贵，而且像所有计算技术一样，可能会遇到收敛问题，特别是在复杂的场景中。这对IST来说是有问题的，因为需要大型模拟队列快速自动运行。最近的研究证明了通过基于学习的方法来加速计算多物理的可行性。所谓的物理信息神经网络(PINN)特别有吸引力，因为它们保证预测符合物理定律，而不仅仅是从数据观测出发。他们还可能通过实施强有力的正规化来减轻对培训数据的要求。该项目将建立在我们现有努力的基础上，开发独特的基于结构和基于流动的Pinn模型。其目的是生产高效和自动化的FSI模拟工具，使TAV血流动力学性能能够在涉及数千名虚拟患者的模拟研究中得到预测。

The project aims to create rapid and scalable deep learning-based simulation techniques for predicting the haemodynamic performance of transcatheter aortic valve (TAV) implants. TAVs are the de facto treatment for severe aortic valve stenosis in patients with med-/high-surgical risk. Yet, various clinical and technological challenges prevent uptake of the procedure in a wider spectrum of patients. Computational modelling tools can allow performance of TAVs in so-far untested scenarios (low-risk groups, bicuspid anatomy, etc.) to be assessed non-invasively and safely. Such capabilities underpin the emerging concept of in-silico trials (ISTs). Complex interactions of blood flow and valves necessitate use of fluid-structure interaction models for haemodynamic assessment. FSI models are expensive, and, like all computational techniques, can encounter convergence problems especially in complex scenarios. This is problematic for ISTs, where large simulation cohorts are required to run quickly and automatically. Recent research demonstrates the feasibility of accelerating computational multiphysics via learning-based approaches. So-called physics-informed neural networks (PINNs) are particularly attractive as they guarantee predictions conform to physical laws, rather than proceeding from data observations only. They may also alleviate training data requirements by imposing strong regularisation. The project will build on our existing efforts to develop distinct structural- and flow-based PINN models. The aim is to produce efficient and automated FSI simulation tools that will enable TAV haemodynamic performance to be predicted in simulation studies involving potentially thousands of virtual patients.