TWINNING: Scalable technologies for creating virtual patient twin populations to accelerate in-silico enabled medical device innovation.

双胞胎：用于创建虚拟患者双胞胎群体的可扩展技术，以加速计算机模拟医疗设备创新。

• 批准号: 10103504
• 金额: $ 86.54万
• 依托单位: ADSILICO LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10103504
• 关键词: TWINNING; Scalable; technologies; creating; virtual

**ad**silico, a spin-out from the University of Leeds (UoL), is aiming to Define, Disrupt and Dominate the in-silico trials space to accelerate medical device innovations. It will become the world's first end-to-end in-silico trials service provider. adsilico's technology for creating virtual populations (VP) of anatomy and physiology builds upon 15+ years of research conducted at CISTIB, Centre for Computational Imaging and Simulation Technologies in Biomedicine from UoL.The team conducted the first in-silico trial (IST) for devices used to treat cerebral aneurysms (Sarrami-Foroushani, A, et al, 2021). The performance of flow diverters was assessed in a VP comprising cerebral vessel and aneurysm geometries and associated blood flow waveforms. The results demonstrated that ISTs can replicate and expand on the insights gained from equivalent real clinical trials, at a fraction of the cost and time, and with no real patient involvement. Impact through industrial translation of the research requires enhancement of the prototype image computing workflows for creating the vessel/aneurysm VPs, so that outputs are of suitable quality and reliability for commercial use.The team has a proven track record of conducting large-scale image analysis studies and building virtual patient populations, especially in the cardiovascular and neurovascular domains. For example, we recently analysed 40k subjects' cardiac magnetic resonance (CMR) images across 50 time points of the cardiac cycle, amounting to 2xmillion CMR image volumes (Xia, Y., et al, 2022). We are the only group in the world to have undertaken such analyses at this scale. This resulted in a curated data set of 2 million 3D full heart geometries and the associated, validated workflows used to create the same.This project aims to refine and translate a prototype for creating high-quality cerebrovascular and aneurysm virtual patient populations that are suitable for use within in-silico studies.**Key deliverables:** 1\. Securing early adopters.2\. Define QA processes for compliance with applicable regulations/standards and for audit purposes.3\. Cerebral aneurysm patient data access, curation/preparation. 4\. Strategy for automating management of primary/derived data.5\. Enhancement/validation of workflow for creating cerebrovascular and aneurysm VPs and design-controlled integration (to execution platform).6\. Create simulation ready VPs for neurovascular devices (target 5000 instances).7\. Validation of VPs with customer-driven exemplar.8\. Design/implement secure environment to access/store/manage digital assets.9\. Contractual frameworks (**ad**silico access to data and customer use of derived data).10\. Determine commercial value of cerebrovascular VPs.11\. Validation of commercial model.

**ad**silico 是利兹大学 (UoL) 的衍生公司，旨在定义、颠覆和主导计算机试验领域，以加速医疗设备创新。它将成为世界上第一家端到端计算机试验服务提供商。 adsilico 用于创建解剖学和生理学虚拟群体 (VP) 的技术建立在伦敦大学生物医学计算成像和模拟技术中心 CISTIB 15 年以上的研究基础之上。该团队对设备进行了首次计算机模拟试验 (IST)用于治疗脑动脉瘤（Sarrami-Foroushani, A, et al, 2021）。分流器的性能在包括脑血管和动脉瘤几何形状以及相关血流波形的 VP 中进行评估。结果表明，IST 可以复制和扩展从同等真实临床试验中获得的见解，而成本和时间仅为一小部分，并且无需真正的患者参与。通过研究的工业转化产生的影响需要增强用于创建血管/动脉瘤 VP 的原型图像计算工作流程，以便输出具有适合商业用途的质量和可靠性。该团队在进行大规模图像分析方面拥有良好的记录研究和建立虚拟患者群体，特别是在心血管和神经血管领域。例如，我们最近分析了 40k 受试者跨心动周期 50 个时间点的心脏磁共振 (CMR) 图像，总计 2x00 万个 CMR 图像量 (Xia, Y., et al, 2022)。我们是世界上唯一进行如此规模的分析的组织。由此产生了包含 200 万个 3D 全心脏几何形状的精选数据集以及用于创建相同数据的相关且经过验证的工作流程。该项目旨在完善和转化原型，以创建适合以下情况的高质量脑血管和动脉瘤虚拟患者群体：在计算机模拟研究中使用。**关键交付成果：** 1\.确保早期采用者的安全。2\.定义 QA 流程，以符合适用的法规/标准并用于审计目的。3\。脑动脉瘤患者数据访问、管理/准备。 4\.主要/派生数据的自动化管理策略。5\。增强/验证用于创建脑血管和动脉瘤 VP 的工作流程以及设计控制的集成（到执行平台）。6\。为神经血管设备创建模拟就绪 VP（目标 5000 个实例）。7\。使用客户驱动的范例验证 VP。8\。设计/实施安全环境来访问/存储/管理数字资产。9\。合同框架（**ad**silico 访问数据和客户使用派生数据）。10\。确定脑血管 VP 的商业价值。11\。商业模式的验证。