Xu Yidan, Brüling Jannes, Carman Laura, Yeung Ted, Besier Thor F, Choisne Julie
Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
Department of Engineering Science and Biomedical Engineering, The University of Auckland, Auckland, New Zealand.
Bone. 2025 Apr;193:117419. doi: 10.1016/j.bone.2025.117419. Epub 2025 Jan 30.
Finite element analysis (FEA) is a widely used tool to predict bone biomechanics in orthopaedics for prevention, treatment, and implant design. Subject-specific FEA models are more accurate than generic adult-scaled models, especially for a paediatric population, due to significant differences in bone geometry and bone mineral density. However, creating these models can be time-consuming, costly and requires medical imaging. To address these limitations, population-based models have been successful in characterizing bone shape and density variation in adults. However, children are not small adults and need their own population-based model to generate accurate and accessible musculoskeletal geometry and bone mineral density in a paediatric population. Therefore, this study aimed to create a biomechanical research tool to predict the personalized shape and density of the paediatric femur using a statistical shape and density model for a population of children aged from 4 to 18 years old. Femur morphology and bone mineral density were extracted from 330 CT scans of children. Variations in shape and density were captured using Principal Component Analysis (PCA). Principal components were correlated to demographic and linear bone measurements to create a predictive statistical shape-density model, which was used to predict femoral shape and density. A leave-one-out analysis showed that the shape-density model can predict the femur geometry with a root mean square error (RMSE) of 1.78 ± 0.46 mm and the bone mineral density with a normalized RMSE ranging from 8.9 % to 13.5 % across various femoral regions. These results underscore the model's potential to reflect real-world physiological variations in the paediatric femur. This statistical shape and density model has the potential for clinical application in rapidly generating personalized computational models using partial or no medical imaging data.
有限元分析（FEA）是一种广泛应用于骨科领域的工具，用于预防、治疗和植入物设计中的骨生物力学预测。由于骨骼几何形状和骨矿物质密度存在显著差异，基于个体的有限元分析模型比一般的成人比例模型更准确，尤其是对于儿科人群。然而，创建这些模型可能耗时、成本高且需要医学成像。为了解决这些局限性，基于人群的模型已成功用于表征成人的骨骼形状和密度变化。然而，儿童并非缩小版的成人，需要有自己基于人群的模型来生成儿科人群准确且可获取的肌肉骨骼几何形状和骨矿物质密度。因此，本研究旨在创建一种生物力学研究工具，使用统计形状和密度模型来预测4至18岁儿童人群的个性化股骨形状和密度。从330例儿童的CT扫描中提取股骨形态和骨矿物质密度。使用主成分分析（PCA）捕捉形状和密度的变化。将主成分与人口统计学和线性骨测量值相关联，以创建预测性统计形状 - 密度模型，该模型用于预测股骨形状和密度。留一法分析表明，形状 - 密度模型可以预测股骨几何形状，均方根误差（RMSE）为1.78±0.46毫米，在不同股骨区域预测骨矿物质密度的归一化RMSE范围为8.9%至13.5%。这些结果强调了该模型反映儿科股骨真实生理变化的潜力。这种统计形状和密度模型有可能在使用部分或无医学成像数据快速生成个性化计算模型方面具有临床应用价值。
