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使用特定于标本的有限元分析并结合随机失效模型,可以准确预测全骨疲劳骨折的概率。

The probability of whole-bone fatigue fracture can be accurately predicted using specimen-specific finite element analysis incorporating a stochastic failure model.

机构信息

Human Performance Laboratory, Faculty of Kinesiology University of Calgary, Canada; McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Canada.

Human Performance Laboratory, Faculty of Kinesiology University of Calgary, Canada.

出版信息

J Biomech. 2022 Oct;143:111273. doi: 10.1016/j.jbiomech.2022.111273. Epub 2022 Aug 28.

Abstract

A better understanding of the mechanisms of mechanical fatigue in bone could help improve understanding of the etiology of stress fractures. Investigations of small material samples of bone have identified a nonlinear relationship between strain magnitude, strained volume, and fatigue life, but it is non-trivial to extend these principles to predict the fatigue-life of whole bones which experience complex loading and non-uniform strain distribution. The purpose of this investigation was to experimentally validate a specimen-specific finite element (FE) model that predicts whole-bone fatigue failure using a stochastic model based on strain magnitude and volume. Thirty-four rabbit tibiae were previously tested to failure under cyclic compression, torsion, or both. Strain distribution during the test was estimated from computed-tomography based specimen-specific FE models, and a stochastic failure model based on strain magnitude and volume was used to predict the probability of failure as a function of loading cycles. Model predicted fracture risk matched experimental observations. Respectively, for the 25%, 50%, 75%, and 95% probabilistic predictions, we observed experimental failure ≤ model predicted values in 41%, 53%, 76%, and 80% of the tested specimens. A Brier scoring rule further demonstrated that this model, using strain magnitude and volume, more accurately predicted failure probability compared to two reference models that considered strain magnitude only. In conclusion, the stochastic model may be a powerful tool in future studies to assess mechanical factors that influence stress fracture risk.

摘要

更好地理解骨骼的机械疲劳机制可以帮助我们更好地理解应力性骨折的病因。对骨骼小样本材料的研究已经确定了应变幅度、应变体积和疲劳寿命之间的非线性关系,但要将这些原理扩展到预测经历复杂载荷和非均匀应变分布的整个骨骼的疲劳寿命并非易事。本研究的目的是通过基于应变幅度和体积的随机模型,对预测整个骨骼疲劳失效的特定于样本的有限元(FE)模型进行实验验证。先前已经有 34 个兔胫骨在循环压缩、扭转或两者的作用下进行了失效测试。通过基于计算机断层扫描的特定于样本的 FE 模型来估计测试过程中的应变分布,并使用基于应变幅度和体积的随机失效模型来预测作为加载周期函数的失效概率。模型预测的骨折风险与实验观察结果相匹配。分别为,对于 25%、50%、75%和 95%的概率预测,我们在 41%、53%、76%和 80%的测试样本中观察到实验性失效≤模型预测值。Brier 评分规则进一步表明,与仅考虑应变幅度的两个参考模型相比,该模型使用应变幅度和体积更准确地预测了失效概率。总之,随机模型可能是未来研究中评估影响应力性骨折风险的机械因素的有力工具。

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