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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：另一种观察骨​​关节炎在骨关节炎中的年臂特性的年龄依赖性

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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：人工神经网络（ANN）模型已经开发出与其小梁骨的关键机械和结构特征相关的严重骨性雄性和雌性标本的年龄。使用双层前馈ANN在多维空间中分析年龄，性别，抗压强度，孔隙率，形态和互连水平之间的复杂相互依赖性。由Levenberg-Marquardt Back传播算法训练，当针对实验数据集进行了优化时，ANN在预测和目标年龄之间实现了r = 96.3％的回归因子。结果表明骨的物理性质的5维向量与标本年龄的强烈相关性。还研究了估算压缩强度作为关键骨折风险的反问题。结果在预测和靶抗压强度之间产生相关性，r = 97.4％的回归因子。在输入数据集的局限内，ANN为硬组织工程决策支持提供了强大的预测模型。

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