Supplementary file 6_Parametrized statistical appearance and shape modelling strategy to predict proximal and diaphyseal femoral fractures.pdf

IntroductionFemoral loading leading to a fracture is known to vary with anthropometry, and patient-specific finite element models have provided important insights into fracture prediction but are often very time consuming to generate. Additionally, existing parametric models do not simultaneously account for variations in both femur geometry and bone density distribution and remain limited to either the femoral shaft or the proximal femur. This inhibits their ability to predict fractures involving both the shaft and proximal regions.
MethodsIn the present study, a novel parametric femur modeling strategy was developed to create whole femur models based on stature, BMI, and age input, including density distribution and geometrical variations, for fracture loading predictions. A statistical shape and appearance femur model was developed based on an input set of CT scans of healthy female femurs (N = 18) between the ages of 50 and 70. Thereafter, multilinear regressions were used to relate principal components to the subject anthropometric characteristics and develop parametric models. The developed parametric models were evaluated using traditional patient-specific models for their potential to represent the influence of changing patient stature, BMI, and age on femoral fractures. Femoral fracture load in three-point bending, axial torsion, and lateral fall cases was predicted using the parametric as well as subject-specific femur models.
ResultsThe developed parametric model was able to predict femoral fracture load variations due to changing anthropometry and age with an average difference of 4.85% compared with predictions using subject-specific models.
DiscussionTherefore, this novel parametric femur model can predict fracture loading while directly incorporating the influence of changing patient anthropometry. In the future, the model could support the development of orthopedic devices tailored to specific patient anthropometries to help mitigate femoral fractures.

引言：已知导致骨折的股骨载荷会随人体测量学特征发生变化，患者特异性有限元模型（finite element model）为骨折预测提供了重要见解，但此类模型的构建通常耗时极久。此外，现有参数化模型无法同时兼顾股骨几何形态与骨密度分布的变化，且仅能应用于股骨干或股骨近端（proximal femur）单一区域，这限制了其对同时累及股骨干与近端区域的骨折的预测能力。
方法：本研究开发了一种全新的参数化股骨建模策略，可基于身高、体重指数（Body Mass Index, BMI）与年龄输入信息，构建包含骨密度分布与几何形态变化的全股骨模型，用于骨折载荷预测。研究基于18名年龄在50至70岁之间的健康女性股骨的计算机断层扫描（Computed Tomography, CT）数据集，构建了统计形状与外观股骨模型。随后，通过多元线性回归将主成分与受试者的人体测量学特征相关联，进而构建参数化模型。研究采用传统患者特异性模型对所构建的参数化模型进行评估，以验证其反映患者身高、BMI与年龄变化对股骨骨折影响的潜力。分别采用参数化模型与患者特异性股骨模型，预测三点弯曲、轴向扭转及侧方跌倒场景下的股骨骨折载荷。
结果：与基于患者特异性模型的预测结果相比，所构建的参数化模型可预测由人体测量学特征与年龄变化引发的股骨骨折载荷变化，两者的平均差异为4.85%。
讨论：综上，这款全新的参数化股骨模型可在直接纳入患者人体测量学特征变化影响的前提下实现骨折载荷预测。未来，该模型可助力适配特定患者人体测量学特征的骨科器械研发，从而助力降低股骨骨折的发生风险。