Literature DB >> 31147202

Evaluation of the capability of the simulated dual energy X-ray absorptiometry-based two-dimensional finite element models for predicting vertebral failure loads.

Yongtao Lu1, Yifan Zhu2, Matthias Krause3, Gerd Huber4, Junyan Li5.   

Abstract

Prediction of the vertebral failure load is of great importance for the prevention and early treatment of bone fracture. However, an efficient and effective method for accurately predicting the failure load of vertebral bones is still lacking. The aim of the present study was to evaluate the capability of the simulated dual energy X-ray absorptiometry (DXA)-based finite element (FE) model for predicting vertebral failure loads. Thirteen dissected spinal segments (T11/T12/L1) were scanned using a HR-pQCT scanner and then were mechanically tested until failure. The subject-specific three-dimensional (3D) and two-dimensional (2D) FE models of T12 were generated from the HR-pQCT scanner and the simulated DXA images, respectively. Additionally, the areal bone mineral density (aBMD) and areal bone mineral content (aBMC) of T12 were calculated. The failure loads predicted by the simulated DXA-based 2D FE models were more moderately correlated with the experimental failure loads (R2 = 0.66) than the aBMC (R2 = 0.61) and aBMD (R2 = 0.56). The 2D FE models were slightly outperformed by the HR-pQCT-based 3D FE models (R2 = 0.71). The present study demonstrated that the simulated DXA-based 2D FE model has better capability for predicting the vertebral failure loads than the densitometric measurements but is outperformed by the 3D FE model. The 2D FE model is more suitable for clinical use due to the low radiation dose and low cost, but it remains to be validated by further in vitro and in vivo studies.
Copyright © 2019. Published by Elsevier Ltd.

Entities:  

Keywords:  BMD; DXA; Finite element analysis; Prediction capability; Vertebral failure

Mesh:

Year:  2019        PMID: 31147202     DOI: 10.1016/j.medengphy.2019.05.007

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  3 in total

1.  A new finite element based parameter to predict bone fracture.

Authors:  Chiara Colombo; Flavia Libonati; Luca Rinaudo; Martina Bellazzi; Fabio Massimo Ulivieri; Laura Vergani
Journal:  PLoS One       Date:  2019-12-05       Impact factor: 3.240

2.  Designing anisotropic porous bone scaffolds using a self-learning convolutional neural network model.

Authors:  Yongtao Lu; Tingxiang Gong; Zhuoyue Yang; Hanxing Zhu; Yadong Liu; Chengwei Wu
Journal:  Front Bioeng Biotechnol       Date:  2022-09-27

3.  Influence of the parameters of the convolutional neural network model in predicting the effective compressive modulus of porous structure.

Authors:  Yongtao Lu; Yi Huo; Zhuoyue Yang; Yibiao Niu; Ming Zhao; Sergei Bosiakov; Lei Li
Journal:  Front Bioeng Biotechnol       Date:  2022-09-15
  3 in total

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