Abstract. We investigate the use of different trabecular bone descriptors and advanced machine learning techniques to complement standard bone mineral density (BMD) measures derived from dual-energy x-ray absorptiometry (DXA) for improving clinical assessment of osteoporotic fracture risk. For this purpose, volumes of interest were extracted from the head, neck, and trochanter of 146 ex vivo proximal femur specimens on multidetector computer tomography. The trabecular bone captured was characterized with (1) statistical moments of the BMD distribution, (2) geometrical features derived from the scaling index method (SIM), and (3) morphometric parameters, such as bone fraction, trabecular thickness, etc. Feature sets comprising DXA BMD and such supplemental features were used to predict the failure load (FL) of the specimens, previously determined through biomechanical testing, with multiregression and support vector regression. Prediction performance was measured by the root mean square error (RMSE); correlation with measured FL was evaluated using the coefficient of determination R2. The best prediction performance was achieved by a combination of DXA BMD and SIM-derived geometric features derived from the femoral head (RMSE: 0.869±0.121, R2: 0.68±0.079), which was significantly better than DXA BMD alone (RMSE: 0.948±0.119, R2: 0.61±0.101) (p<10−4). For multivariate feature sets, SVR outperformed multiregression (p<0.05). These results suggest that supplementing standard DXA BMD measurements with sophisticated femoral trabecular bone characterization and supervised learning techniques can significantly improve biomechanical strength prediction in proximal femur specimens.

中文翻译：

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通过小梁骨微结构的几何特征和支持向量回归改进人类股骨近端标本的骨强度预测

摘要。我们研究了使用不同的小梁骨描述符和先进的机器学习技术来补充从双能 X 射线吸收测定法 (DXA) 得出的标准骨矿物质密度 (BMD) 测量值，以改善骨质疏松性骨折风险的临床评估。为此，在多探测器计算机断层扫描上从 146 个离体股骨近端标本的头部、颈部和转子中提取了感兴趣的体积。捕获的小梁骨具有以下特征：(1) BMD 分布的统计矩，(2) 来自缩放指数方​​法 (SIM) 的几何特征，以及 (3) 形态测量参数，例如骨分数、小梁厚度等。 特征包含 DXA BMD 和此类补充特征的集合用于预测试样的失效载荷 (FL)，先前通过生物力学测试确定，使用多重回归和支持向量回归。预测性能由均方根误差 (RMSE) 衡量；使用决定系数 R2 评估与测量 FL 的相关性。结合DXA BMD和源自股骨头的SIM衍生几何特征（RMSE：0.869±0.121，R2：0.68±0.079）实现了最佳预测性能，明显优于单独的DXA BMD（RMSE：0.948± 0.119，R2：0.61±0.101) (p<10-4)。对于多元特征集，SVR 优于多元回归 (p<0.05)。