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Neuro-musculoskeletal flexible multibody simulation yields a framework for efficient bone failure risk assessment

机译:神经肌肉骨骼灵活的多体仿真为有效的骨衰竭风险评估提供了一个框架

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摘要

Fragility fractures are a major socioeconomic problem. A non-invasive, computationally-efficient method for the identification of fracture risk scenarios under the representation of neuro-musculoskeletal dynamics does not exist. We introduce a computational workflow that integrates modally-reduced, quantitative CT-based finite-element models into neuro-musculoskeletal flexible multibody simulation (NfMBS) for early bone fracture risk assessment. Our workflow quantifies the bone strength via the osteogenic stresses and strains that arise due to the physiological-like loading of the bone under the representation of patient-specific neuro-musculoskeletal dynamics. This allows for non-invasive, computationally-efficient dynamic analysis over the enormous parameter space of fracture risk scenarios, while requiring only sparse clinical data. Experimental validation on a fresh human femur specimen together with femur strength computations that were consistent with literature findings provide confidence in the workflow: The simulation of an entire squat took only 38 s CPU-time. Owing to the loss (16% cortical, 33% trabecular) of bone mineral density (BMD), the strain measure that is associated with bone fracture increased by 31.4%; and yielded an elevated risk of a femoral hip fracture. Our novel workflow could offer clinicians with decision-making guidance by enabling the first combined in-silico analysis tool using NfMBS and BMD measurements for optimized bone fracture risk assessment.
机译:脆性骨折是一个主要的社会经济问题。在神经肌肉骨骼动力学的表征下,没有一种用于确定骨折风险的非侵入性,计算效率高的方法。我们引入了一种计算流程,该流程将基于模态缩减的定量基于CT的有限元模型集成到神经肌肉骨骼柔性多体仿真(NfMBS)中,以进行早期骨折风险评估。我们的工作流程通过在特定于患者的神经肌肉骨骼动力学表示的情况下,由于骨骼的生理样负载而产生的成骨应力和应变来量化骨骼强度。这允许在骨折风险情景的巨大参数空间上进行非侵入性,计算效率高的动态分析,而仅需要稀疏的临床数据。在新鲜的人股骨标本上进行的实验验证以及与文献发现相符的股骨强度计算为工作流程提供了信心:整个蹲坐的模拟仅花费了38s CPU时间。由于骨矿物质密度(BMD)的损失(皮质骨减少16%,小梁骨减少33%),与骨折相关的应变测量增加了31.4%。并增加了股骨髋部骨折的风险。我们新颖的工作流程可通过启用第一个结合使用NfMBS和BMD测量的硅内分析工具来优化骨折风险评估,从而为临床医生提供决策指导。

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