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Prediction of Elastic Behavior of Human Trabecular Bone Using A DXA Image-Based Deep Learning Model

  • Multiscale Experiments and Modeling in Biomaterials and Biological Materials
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Abstract

Inspired by the recent advancement in deep learning (DL) techniques, this study intended to confirm whether DL models could be trained to predict the elastic behavior of trabecular bone, a highly hierarchical biological material, using its dual-energy x-ray absorptiometry (DXA) images. The convolutional neural network, the most successful DL model in imaging-based predictions, was trained using simulated DXA images of trabecular bone samples as input and their apparent elastic modulus (Eapparent) determined using microCT-based finite element simulations as output (label). The results showed that the DL model achieved high fidelity in predicting Eapparent of trabecular bone samples (R2 > 0.86), and its performance appeared to be better than that of histomorphometric parameter-based regression models built using the same bone samples. The outcome of this study suggests that DXA image-based DL techniques can be used for multiscale modeling of trabecular bone to predict its elastic behavior.

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Acknowledgements

The authors are grateful to Mr. James Schmitz at UT Health San Antonio for technical assistance in the acquisition of microCT scans of human cadaveric proximal femur samples.

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Authors and Affiliations

  1. Mechanical Engineering, University of Texas at San Antonio, San Antonio, Texas, 78249, United States

    Pengwei Xiao, Eakeen Haque, Trenten Wahlen & Xiaodu Wang

  2. Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, Texas, 78249, United States

    Tinghe Zhang & Yufei Huang

  3. Health and Kinesiology, University of Texas at Tyler, Tyler, Texas, 75799, United States

    X. Neil Dong

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  1. Pengwei Xiao
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  2. Tinghe Zhang
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Correspondence to Yufei Huang or Xiaodu Wang.

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Xiao, P., Zhang, T., Haque, E. et al. Prediction of Elastic Behavior of Human Trabecular Bone Using A DXA Image-Based Deep Learning Model. JOM 73, 2366–2376 (2021). https://doi.org/10.1007/s11837-021-04704-z

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  • DOI: https://doi.org/10.1007/s11837-021-04704-z

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