Neural networks for analysis of trabecular bone in osteoarthritis
Neural networks for analysis of trabecular bone in osteoarthritis
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DOI：
10.1680/bbn.14.00006
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发表时间：
2015-01-01
影响因子：
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通讯作者：
Mallick, Kajal K.
中科院分区：
文献类型：
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作者：
Khovanova, Natalia A.;Shaikhina, Torgyn;Mallick, Kajal K.
本研究调查了男性和女性标本的年龄与骨小梁的物理力学性质的相关性，包括抗压强度、骨体积分数、结构模型指数、骨小梁厚度因子、连通性水平和孔隙形态。为了解决多维空间中关键参数之间的复杂相互依赖关系，设计了一个人工神经网络来分析35个可用样本。该网络通过Levenberg-MarQuardt反向传播算法进行训练，优化后的回归系数达到0.96，表明年龄与严重骨关节炎影响的骨骼的物理性质有很强的相关性。此外，抗压强度是预测骨老化的最重要因素。在输入数据集的限制下，所开发的模型为组织工程应用提供了可靠的预测工具。
This study investigated the correlation of age in male and female specimens with physico-mechanical properties of trabecular bone including compressive strength, bone volume fraction, structural model index, trabecular thickness factor, level of inter-connectivity and pore morphology. An artificial neural network was designed to analyse 35 available samples in order to account for complex inter-dependencies of the key parameters in multi-dimensional space. Trained by using Levenberg-Marquardt back propagation algorithm, the network achieved regression factor of 0.96 by optimisation and showed that age correlates strongly with the physical properties of the bone affected by severe osteoarthritis. In addition, the compressive strength was found to be the most important factor for predicting the bone aging. Within the limitations of the input data set, the model developed provides a reliable predictive tool to tissue engineering applications.