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首页> 外文会议>IEEE-EMBS International Conference on Biomedical and Health Informatics >Artificial neural networks in hard tissue engineering: Another look at age-dependence of trabecular bone properties in osteoarthritis
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Artificial neural networks in hard tissue engineering: Another look at age-dependence of trabecular bone properties in osteoarthritis

机译：硬组织工程中的人工神经网络：骨关节炎中小梁骨特性的年龄依赖性

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Artificial Neural Network (ANN) model has been developed to correlate age of severely osteoarthritic male and female specimens with key mechanical and structural characteristics of their trabecular bone. The complex interdependency between age, gender, compressive strength, porosity, morphology and level of interconnectivity was analysed in multi-dimensional space using a two-layer feedforward ANN. Trained by Levenberg-Marquardt back propagation algorithm, the ANN achieved regression factor of R = 96.3% between the predicted and target age when optimised for the experimental dataset. Results indicate a strong correlation of the 5-dimensional vector of physical properties of the bone with the age of the specimens. The inverse problem of estimating compressive strength as the key bone fracture risk was also investigated. The outcomes yield correlation between predicted and target compressive strength with the regression factor of R = 97.4%. Within the limitations of the input data set, the ANNs provide robust predictive models for hard tissue engineering decision support.
机译：已经开发了人工神经网络（ANN）模型，以将严重骨关节炎的男性和女性标本的年龄与其骨小梁的关键机械和结构特征相关联。使用两层前馈ANN在多维空间中分析了年龄，性别，抗压强度，孔隙率，形态和互连程度之间的复杂相互依赖性。经过Levenberg-Marquardt反向传播算法的训练，当针对实验数据集进行优化时，ANN在预测年龄和目标年龄之间达到了R = 96.3％的回归因子。结果表明，骨骼物理特性的5维向量与标本的年龄密切相关。还研究了将抗压强度作为关键骨折风险的反问题。结果得出预测抗压强度与目标抗压强度之间的相关性，回归因子R = 97.4％。在输入数据集的限制内，人工神经网络为硬组织工程决策支持提供了可靠的预测模型。

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