Reproducibility in simulation-based prediction of natural knee mechanics

项目总结 膝关节生物力学的模拟越来越多地被用于开发个性化和可操作的知识 关于健康的动态平衡、肌肉骨骼疾病的影响和受伤风险。计算建模 在预测植入物的性能和安全性方面也很有吸引力，并为外科组织重建和 康复策略。这是生物医学中无处不在的建模和模拟的自然结果，而且它 其动机是膝盖是肌肉骨骼医疗保健中的首要问题。然而，从头到尾和转弯- 机械模拟的关键例子，以加速生物医学发现并为临床护理提供常规信息， 是稀有的。具体地说，在膝关节生物力学中，建模和仿真的令人信服的承诺并没有 满足感。计算模型的交付及其预期使用需要许多操作，这些操作 定义建模和仿真工作流。建模者选择的变化和其选择的模棱两可 实现引入了影响整个建模和仿真生命周期的重现性的不确定性， 从中间产品到终端模拟结果。可重复性对更广泛的可信度至关重要 模特儿实践。根据模型的预期用途，它不在可接受的阈值内是一种 采用模拟的重大障碍。膝盖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 膝关节生物力学中的建模和仿真艺术。从相同的数据开始，目的是 模拟相同的模型用例，五个团队演示了每个团队的流程与其他团队的不同之处。 这导致了组织结构的解剖和机械表示的不同，大多数 重要的是，模拟预测。在提议的项目中，我们首先致力于确定达成共识的工作流程 代表了计算膝关节生物力学方面的良好实践。将就以下问题达成共识： 作为可信度框架使用的各种模型背景已经承认了深度的依赖性 以及建模活动对模型预期用途的影响。使用的上下文还规定了 模型、构建模型并验证其预测、计算资源和主题的数据量- 专一性。德尔菲法将促进建立共识，这将为 与世界各地的膝部建模专家进行有条理的迭代推广。在第二个目标中，重复性 并将对照所需的模拟解决方案测试与情景相关的共识工作流的准确性 产出。五个团队将以相同的意图，使用相同的数据开发模型并进行模拟，以及 这一次，依赖于相同的建模和仿真工作流。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 膝关节建模方法的生态系统碎片化。随后，它们将实现多站点、大规模和 在膝关节生物力学的计算机实验中值得信赖。

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.