Reproducibility in simulation-based prediction of natural knee mechanics

项目摘要 膝关节生物力学的模拟越来越多地用于开发个性化和可操作的知识 关于健康的体内平衡、肌肉骨骼疾病的影响和受伤的风险。计算建模 还可用于预测植入物的性能和安全性，并为外科组织重建提供信息， 康复战略。这是建模和仿真在生物医学中无处不在的自然结果， 膝关节是肌肉骨骼保健的主要关注点。然而，从始至终， 机械模拟的关键例子，以加速生物医学发现，并定期通知临床护理， 是稀缺的特别是在膝关节生物力学中，建模和模拟的令人信服的承诺尚未得到 很满足计算模型及其预期利用的交付需要许多操作，这些操作共同 定义建模和仿真工作流。建模者的选择的变化和它们的模糊性 实现引入了影响建模和仿真生命周期中再现性的不确定性， 从中间产品到终端模拟结果。复制性对于更广泛的信誉至关重要 的模式实践。它的缺席在一个可接受的阈值内，由模型的预期用途决定， 采用模拟的重大障碍。膝关节模型的可扩展使用也受到以下负担的阻碍： 建模和仿真活动，缺乏专门的指导。在过去的颁奖期间，我们记录了 膝关节生物力学建模和仿真的艺术。从相同的数据开始， 通过模拟相同的模型用例，五个团队演示了每个团队的过程与其他团队的不同之处。 这导致了组织结构的解剖学和机械表现的差异， 重要的是模拟预测。在拟议的项目中，我们首先旨在确定共识工作流程， 代表了计算膝关节生物力学的良好实践。协商一致意见将建立具体到一个 作为可信度框架使用的各种模型上下文集已经承认了深度的依赖性 和强度的建模活动对模型的预期用途。使用环境也决定了 模型、用于构建模型并验证其预测的数据量、计算资源和主题- 的特异性德尔菲法将有助于达成共识， 与全球膝关节建模专家进行结构化和迭代式外联。在第二个目标中， 将根据模拟所需的分辨率测试上下文相关共识工作流的准确性 产出五个团队将开发模型，并使用相同的数据进行模拟，具有相同的意图， 这一次，依赖于相同的建模和仿真工作流程。记录和传播共识 工作流，包括用例的结果和副产品，将统一并可能标准化高度 膝关节建模方法的零散生态系统。随后，它们将实现多站点、大规模和 膝关节生物力学的可靠计算机模拟试验。

PROJECT SUMMARY Simulations of knee biomechanics are increasingly utilized to develop individualized and actionable knowledge regarding healthy homeostasis, impact of musculoskeletal diseases, and risk of injury. Computational modeling is also appealing to predict performance and safety of implants, and informs surgical tissue reconstruction and rehabilitation strategies. This is a natural result of the ubiquity of modeling and simulation in biomedicine, and it is motivated by the knee being a primary concern in musculoskeletal healthcare. However, start-to-end and turn- key examples of mechanistic simulations, to accelerate biomedical discovery and to routinely inform clinical care, are scarce. In knee biomechanics specifically, the compelling promise of modeling and simulation has not been fulfilled. Delivery of computational models and their intended utilization require many operations that collectively define the modeling and simulation workflow. Variations in the modeler’s choices and the ambiguity of their implementation introduce uncertainties that impact reproducibility across the modeling and simulation lifecycle, from intermediate products to end-point simulation results. Reproducibility is essential to the broader credibility of modeling practices. Its absence within an acceptable threshold, as dictated by the model’s intended use, is a significant barrier for adoption of simulation. Scalable uses of knee models are also impeded by the burden of modeling and simulation activities and a lack of specialized guidance. In the past award period, we documented the art of modeling and simulation in knee biomechanics. Starting with the same data and with the intent to simulate the same model use cases, five teams demonstrated how each team’s processes vary from others. This resulted in differences in anatomical and mechanical representations of tissue structures and, most importantly, simulation predictions. In the proposed project, we first aim to determine consensus workflows that represent good practices in computational knee biomechanics. Consensus will be established specific to a diverse set of model contexts of use as credibility frameworks already acknowledge the dependence of the depth and intensity of modeling activities on the intended use of models. Context of use also dictates the fidelity of the model, the amount of data to build the model and validate its predictions, computing resources, and subject- specificity. Consensus building will be facilitated by the Delphi method, which will provide the means for a structured and iterative outreach to the knee modeling experts worldwide. In the second aim, the reproducibility and accuracy of context relevant consensus workflows will be tested against required resolution of simulation outputs. Five teams will develop models and conduct simulations using the same data, with the same intent, and this time, relying on the same modeling and simulation workflow. Documented and disseminated consensus workflows, including outcomes and by-products of use cases, will unify, and possibly standardize, the highly fragmented ecosystem of knee modeling approaches. Subsequently, they will enable multi-site, large scale, and dependable in silico trials in knee biomechanics.