Cardiovascular Device Innovation & Regulatory Science: Virtual Chimaeras and In-Silico Trials with novel Hybrid Machine Learning

INSILICO将建立第一个结合数据和知识驱动的机器学习的集成框架，实现医疗设备（MDs）中的硅内试验（ISTs）。通过降低研发成本和加快监管审批速度，有关MD安全性和有效性的新型芯片见解将显著影响监管科学和创新。我提出了一种新的方法，将ist设想为虚拟实验的多模型集成空间，相当于招募一组合成的、可验证的现实虚拟患者（vp）。每个VP将在生理包膜内植入一个虚拟的MD，模拟患者的短期/长期反应。MD的性能和设计将在不同的生理状态下进行预测，突出了仅在后期临床试验中遇到的不确定性。INSILICO将克服3个高风险、高影响的技术障碍：1)创建反映各种解剖、生理和病理的虚拟患者队列，从真实患者群体中摄取真实数据；2)准确预测虚拟人群的介入结果；3)在考虑任意/认知不确定性的同时确保计算预测的可靠性和可扩展性。提出的统一的物理知情图学习方案将有助于生成vp和物理一致的模拟。该项目将1)引入虚拟嵌合体的概念，2)在图网络上扩展物理信息学习，以构建新的可靠、准确和快速的多物理场模拟器，以及3)重新制定一个独特的行业提供的试验数据集，以增加行业、试验人员和监管机构对ist的信任。INSILICO支持下一代ist，这是一种超越当前传统临床试验的范式转变，是MD安全性和有效性科学证据的主要来源。insilicon将从根本上改变医学监管科学和创新。

INSILICO will establish the first integrated framework combining data- and knowledge-driven machine learning, realising in-silico trials (ISTs) in medical devices (MDs). Novel in-silico insights on MD safety and efficacy will impact regulatory science and innovation significantly by reducing R&D costs and speeding up regulatory clearance.I propose a new way to conceive ISTs as multi-model ensemble spaces of virtual experiments, equivalent to enrolling a cohort of synthetic, verifiably realistic virtual patients (VPs). Each VP will harbour a virtually implanted MD operating within physiological envelopes, modelling the patient's short-/long-term response. MD's performance and design will be predicted under diverse physiological regimes, highlighting uncertainties only encountered in late-phase clinical testing. INSILICO will overcome 3 high-risk high-impact technical barriers by 1) creating virtual patient cohorts reflecting various anatomy, physiology, and pathology ingesting real-world data from real patient populations, 2) accurately predicting interventional outcomes in virtual populations, 3) ensuring the reliability and scalability of computational predictions while accounting for aleatoric/epistemic uncertainties.The proposed unified physics-informed graph learning scheme will facilitate both the generation of VPs and physically consistent simulations. This project will 1) introduce the concept of virtual chimaeras, 2) extend physics-informed learning over graph networks to construct new reliable, accurate and fast multiphysics simulators, and 3) re-enact a unique industry-provided trial dataset to grow trust in ISTs by industry, trialists and regulators. INSILICO underpins next-generation ISTs, a paradigm shift beyond current conventional clinical trials as the primary source of scientific evidence on MD safety and efficacy. INSILICO will fundamentally transform MD regulatory science and innovation.