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Can DXA image-based deep learning model predict the anisotropic elastic behavior of trabecular bone?
Journal of the Mechanical Behavior of Biomedical Materials ( IF 3.9 ) Pub Date : 2021-09-15 , DOI: 10.1016/j.jmbbm.2021.104834
Pengwei Xiao 1 , Eakeen Haque 1 , Tinghe Zhang 2 , X Neil Dong 3 , Yufei Huang 2 , Xiaodu Wang 1
Affiliation  

3D image-based finite element (FE) and bone volume fraction (BV/TV)/fabric tensor modeling techniques are currently used to determine the apparent stiffness tensor of trabecular bone for assessing its anisotropic elastic behavior. Inspired by the recent success of deep learning (DL) techniques, we hypothesized that DL modeling techniques could be used to predict the apparent stiffness tensor of trabecular bone directly using dual-energy X-ray absorptiometry (DXA) images. To test the hypothesis, a convolutional neural network (CNN) model was trained and validated to predict the apparent stiffness tensor of trabecular bone cubes using their DXA images. Trabecular bone cubes obtained from human cadaver proximal femurs were used to obtain simulated DXA images as input, and the apparent stiffness tensor of the trabecular cubes determined by using micro-CT based FE simulations was used as output (ground truth) to train the DL model. The prediction accuracy of the DL model was evaluated by comparing it with the micro-CT based FE models, histomorphometric parameter based multiple linear regression models, and BV/TV/fabric tensor based multiple linear regression models. The results showed that DXA image-based DL model achieved high fidelity in predicting the apparent stiffness tensor of trabecular bone cubes (R2 = 0.905–0.973), comparable to or better than the histomorphometric parameter based multiple linear regression and BV/TV/fabric tensor based multiple linear regression models, thus supporting the hypothesis of this study. The outcome of this study could be used to help develop DXA image-based DL techniques for clinical assessment of bone fracture risk.



中文翻译:

基于DXA图像的深度学习模型能否预测小梁骨的各向异性弹性行为?

基于 3D 图像的有限元 (FE) 和骨体积分数 ( BV/TV))/织物张量建模技术目前用于确定小梁骨的表观刚度张量,以评估其各向异性弹性行为。受最近深度学习 (DL) 技术成功的启发,我们假设 DL 建模技术可用于直接使用双能 X 射线吸收测量 (DXA) 图像预测骨小梁的表观刚度张量。为了验证该假设,我们训练并验证了卷积神经网络 (CNN) 模型,以使用其 DXA 图像预测小梁骨立方体的表观刚度张量。从人类尸体近端股骨获得的小梁骨立方体用于获得模拟 DXA 图像作为输入,使用基于微 CT 的 FE 模拟确定的小梁立方体的表观刚度张量用作输出(地面实况)来训练 DL 模型。通过与基于微 CT 的 FE 模型、基于组织形态测量参数的多元线性回归模型和基于BV/TV /fabric 张量的多元线性回归模型。结果表明,基于 DXA 图像的 DL 模型在预测小梁骨立方体的表观刚度张量(R 2  = 0.905–0.973)方面实现了高保真度,与基于多元线性回归和BV/TV /fabric的组织形态测量参数相当或更好基于张量的多元线性回归模型,从而支持了本研究的假设。这项研究的结果可用于帮助开发基于 DXA 图像的 DL 技术,用于临床评估骨折风险。

更新日期:2021-09-19
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