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（MD）中的计算机模拟试验（IST）。新的硅见解MD安全性和有效性将影响监管科学和创新显着降低研发成本和加快监管clearance.I提出了一种新的方式来构思的虚拟实验的多模型集成空间的IST，相当于注册一个队列的合成，可验证的现实虚拟患者（VP）。每个VP都将包含一个在生理包络内运行的虚拟植入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，突出显示仅在后期临床测试中遇到的不确定性。INSILICO将通过以下方式克服3个高风险、高影响的技术障碍：1）根据来自真实的患者人群的真实世界数据创建反映各种解剖学、生理学和病理学的虚拟患者队列，2）准确预测虚拟人群的介入结局，3）确保计算预测的可靠性和可扩展性，同时考虑任意/认知的不确定性。知情图学习方案将促进VP的生成和物理上一致的模拟。该项目将1）引入虚拟嵌合体的概念，2）通过图形网络扩展物理学知识学习，以构建新的可靠，准确和快速的多物理场模拟器，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，试验者和监管机构对IST的信任。INSILICO支持下一代IST，这是超越当前常规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。INSILICO将从根本上改变MD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.