Details

Autor(en) / Beteiligte

• Xiao, Pengwei
• Zhang, Tinghe
• Haque, Eakeen
• Wahlen, Trenten
• Dong, X. Neil
• Huang, Yufei
• Wang, Xiaodu

Titel

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

Ist Teil von

• JOM (1989), 2021-08, Vol.73 (8), p.2366-2376

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2021

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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 ( E apparent ) determined using microCT-based finite element simulations as output (label). The results showed that the DL model achieved high fidelity in predicting E apparent of trabecular bone samples ( R 2 > 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.