Predicting the effective compressive modulus of human cancellous bone using the convolutional neural network method

References

• Alastruey-Lopez D, Ezquerra L, Seral B, Perez MA. 2020. Using artificial neural networks to predict impingement and dislocation in total hip arthroplasty. Comput Method Biomech. 23(4):1–9.
   PubMedGoogle Scholar
• Alber M, Buganza TA, Cannon WR, De S, Dura S, Garikipati K, Karniadakis G, Lytton WW, Perdikaris P, Petzold L, et al. 2019. Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ Digit Med. 2(1):1–11.
   PubMedGoogle Scholar
• ANSYS, Inc. 1994. ANSYS 56 manual: 2-D and axisymmetric solids. Canonsburg, PA: ANSYS, Inc.
   Google Scholar
• Bhattacharya P, Altai Z, Qasim M, Viceconti M. 2019. A multiscale model to predict current absolute risk of femoral fracture in a postmenopausal population. Biomech Model Mechanobiol. 18(2):301–318.
   PubMed Web of Science ®Google Scholar
• Chandran V, Maquer G, Gerig T, Zysset P, Reyes M. 2019. Supervised learning for bone shape and cortical thickness estimation from CT images for finite element analysis. Med Image Anal. 52:42–55.
   PubMed Web of Science ®Google Scholar
• Chevalier Y, Quek E, Borah B, Gross G, Stewart J, Lang T, Zysset P. 2010. Biomechanical effects of teriparatide in women with osteoporosis treated previously with alendronate and risedronate: results from quantitative computed tomography-based finite element analysis of the vertebral body. Bone. 46(1):41–48.
   PubMed Web of Science ®Google Scholar
• Crawford RP, Cann CE, Keaveny TM. 2003. Finite element models predict in vitro vertebral body compressive strength better than quantitative computed tomography. Bone. 33(4):744–750.
   PubMed Web of Science ®Google Scholar
• Dong XN, Lu YT, Krause M, Huber G, Chevalier Y, Leng H, Maquer G. 2018. Variogram-based evaluations of DXA correlate with vertebral strength, but do not enhance the prediction compared to aBMD alone. J Biomech. 77:223–227.
   PubMed Web of Science ®Google Scholar
• Ebbesen EN, Thomsen JS, Beck H, Nepper HJ, Mosekilde L. 1999. Lumbar vertebral body compressive strength evaluated by dual-energy X-ray absorptiometry, quantitative computed tomography, and ashing. Bone. 25(6):713–724.
   PubMed Web of Science ®Google Scholar
• Jiang P, Missoum S, Chen Z. 2015. Fusion of clinical and stochastic finite element data for hip fracture risk prediction. J Biomech. 48(15):4043–4052.
   PubMed Web of Science ®Google Scholar
• Knowles NK, Reeves J, Ferreira LM. 2016. Quantitative computed tomography (QCT) derived bone mineral density in finite element studies: a review of the literature. J Exp Orthop. 3(1):36.
   PubMedGoogle Scholar
• Liang L, Liu M, Martin C, Sun W. 2018. A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite element analysis. J R Soc Interface. 15(138):20170844.
   PubMed Web of Science ®Google Scholar
• Li X, Liu ZL, Cui SQ, Luo CC, Li CF, Zhuang Z. 2019. Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning. Comput Methods Appl Mech Eng. 347:735–753.
   Web of Science ®Google Scholar
• Li GY, Wang HR, Zhang MZ, Tupin S, Qiao A, Liu YJ, Ohta H, Anzai H. 2021. Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning. Commun Biol. 4(1):1–12.
   PubMed Web of Science ®Google Scholar
• Lochmüller E-M, Bürklein D, Kuhn V, Glaser C, Müller R, Glüer CC, Eckstein F. 2002. Mechanical strength of the thoracolumbar spine in the elderly: prediction from in situ dual-energy X-ray absorptiometry, quantitative computed tomography (QCT), upper and lower limb peripheral QCT, and quantitative ultrasound. Bone. 31(1):77–84.
   PubMed Web of Science ®Google Scholar
• Lu YT, Krause M, Bishop N, Sellenschloh K, Gluer CC, Puschel K, Amling M, Morlock MM, Huber G. 2015. The role of patient-mode high-resolution peripheral quantitative computed tomography indices in the prediction of failure strength of the elderly women’s thoracic vertebral body. Osteoporos Int. 26(1):237–244.
   PubMed Web of Science ®Google Scholar
• Lu YT, Maquer G, Museyko O, Puschel K, Engelke K, Zysset P, Michael M, Huber G. 2014. Finite element analyses of human vertebral bodies embedded in polymethylmethalcrylate or loaded via the hyperelastic intervertebral disc models provide equivalent predictions of experimental strength. J Biomech. 47(10):2512–2516.
   PubMed Web of Science ®Google Scholar
• Lu YT, Zhu YF, Krause M, Huber G, Li JY. 2019. Evaluation of the capability of the simulated dual energy X-ray absorptiometry-based two-dimensional finite element models for predicting vertebral failure loads. Med Eng Phys. 69:43–49.
   PubMed Web of Science ®Google Scholar
• Metz C, Duda G, Checa S. 2020. Towards multi-dynamic mechano-biological optimization of 3D-printed scaffolds to foster bone regeneration. Acta Biomater. 101:117–127.
   PubMed Web of Science ®Google Scholar
• Neto MA, Yu W, Tang T, Leal R. 2010. Analysis and optimization of the heterogeneous materials using the variational asymptotic method for unit cell homogenization. Compos Struct. 92(12):2946–2954.
   Web of Science ®Google Scholar
• Pistoia W, Rietbergen B, Lochmuller EM, Lill CA, Eckstein F, Ruegsegger P. 2002. Estimation of distal radius failure load with micro-finite element analysis models based on three-dimensional peripheral quantitative computed tomography images. Bone. 30(6):842–848.
   PubMed Web of Science ®Google Scholar
• Rane L, Ding Z, McGregor AH, Bull AM. 2019. Deep learning for musculoskeletal force prediction. Ann Biomed Eng. 47(3):778–789.
   PubMed Web of Science ®Google Scholar
• Rubio J, Angelov P, Pacheco J. 2011. Uniformly stable backpropagation algorithm to train a feedforward neural network. IEEE Trans Neural Netw. 22(3):356–366.
   PubMedGoogle Scholar
• Wang X, Sanyal A, Cawthon PM, Palermo L, Jekir M, Christensen J, Ensrud KE, Cummings SR, Orwoll E, Black DM, Osteoporotic Fractures in Men (MrOS) Research Group, et al. 2012. Prediction of new clinical vertebral fractures in elderly men using finite element analysis of CT scans. J Bone Miner Res. 27(4):808–816.
   PubMed Web of Science ®Google Scholar
• Ye S, Li B, Li Q, Zhao HP, Feng XQ. 2019. Deep neural network method for predicting the mechanical properties of composites. Appl Phys Lett. 115(16):161901.
   Web of Science ®Google Scholar