基于骨小梁形态的神经网络载荷预测
Neural network prediction of load from the morphology of trabecular bone
摘要：
骨适应模型通常在正向方向上求解，这意味着通过运行骨组织适应模型来确定骨对给定载荷集的响应。该模型通常使用诸如有限元模型的数值技术来求解。相反，人们可能对导致给定骨状态的载荷感兴趣。这是前一个问题的逆问题。尽管逆问题有多种应用，但它并没有像正问题那样受到如此多的关注，部分原因是求解逆问题更加困难。求解逆问题需要一种非线性系统辨识技术。在这项研究中，我们使用人工神经网络预测组织适应负荷从一个给定的密度分布的骨小梁。结果表明，所提出的方法可以成功地识别加载参数的密度分布的组织。所有负载预测算法的两个重要挑战是逆问题的解的非唯一性和组织形态的测量的不准确性。这两个挑战进行了研究，它表明，本文提出的负荷预测技术可以克服这两个。
英文摘要：
Bone adaptation models are often solved in the forward direction, meaning that the response of bone to a given set of loads is determined by running a bone tissue adaptation model. The model is generally solved using a numerical technique such as the finite element model. Conversely, one may be interested in the loads that have resulted in a given state of bone. This is the inverse of the former problem. Even though the inverse problem has several applications, it has not received as much attention as the forward problem, partly because solving the inverse problem is more difficult. A nonlinear system identification technique is needed for solving the inverse problem. In this study, we use artificial neural networks for prediction of tissue adaptation loads from a given density distribution of trabecular bone. It is shown that the proposed method can successfully identify the loading parameters from the density distribution of the tissue. Two important challenges for all load prediction algorithms are the non-uniqueness of the solution of the inverse problem and the inaccuracies in the measurement of the morphology of the tissue. Both challenges are studied, and it is shown that the load prediction technique proposed in this paper can overcome both.
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