Estimating the mechanical competence parameter of the trabecular bone: a neural network approach
Filletti, Érica Regina; Roque, Waldir Leite
http://dx.doi.org/10.1590/2446-4740.05615
Res. Biomed. Eng., vol.32, n2, p.137-143, 2016
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Abstract
Introduction: The mechanical competence parameter (MCP) of the trabecular bone is a parameter that merges the volume fraction, connectivity, tortuosity and Young modulus of elasticity, to provide a single measure of the trabecular bone structural quality. Methods: As the MCP is estimated for 3D images and the Young modulus simulations are quite consuming, in this paper, an alternative approach to estimate the MCP based on artificial neural network (ANN) is discussed considering as the training set a group of 23 in vitro vertebrae and 12 distal radius samples obtained by microcomputed tomography (μCT), and 83 in vivo distal radius magnetic resonance image samples (MRI). Results: It is shown that the ANN was able to predict with very high accuracy the MCP for 29 new samples, being 6 vertebrae and 3 distal radius bones by μCT and 20 distal radius bone by MRI. Conclusion: There is a strong correlation (R2 = 0.97) between both techniques and, despite the small number of testing samples, the Bland-Altman analysis shows that ANN is within the limits of agreement to estimate the MCP.
Keywords
Osteoporosis, Trabecular bone, Mechanical competence, Artificial neural network, Machine learning.
References
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Christopher JJ, Ramakrishnan S. Assessment and classification of mechanical strength components of human femur trabecular bone using digital image processing and neural networks. Journal of Mechanics in Medicine and Biology. 2007; 7(3):315-24. http://dx.doi.org/10.1142/S0219519407002339.
Dimai HP, Redlich K, Peretz M, Borgström F, Siebert U, Mahlich J. Economic burden of osteoporotic fractures in Austria. Health Economics Review. 2012; 2(1):12. http://dx.doi.org/10.1186/2191-1991-2-12. PMid:22827971.
Filletti ER, Roque WL. Neural network prediction of the trabecular bone mechanical competence parameter. In: Braidot A, Hadad A, editors. IFMBE Proceedings; 2014 Oct 29-31; Paraná. Cham: Springer International Publishing; 2015. v. 49, p. 226-29.
Gregory JS, Junold RM, Undrill PE, Aspden RM. Analysis of trabecular bone structure using fourier transforms and neural networks. IEEE Transactions on Information Technology in Biomedicine. 1999; 3(4):289-94. http://dx.doi.org/10.1109/4233.809173. PMid:10719479.
Hambli R. Multiscale prediction of crack density and crack length accumulation in trabecular bone based on neural networks and nite element simulation. International Journal for Numerical Methods in Biomedical Engineering. 2011; 27(4):461-75. http://dx.doi.org/10.1002/cnm.1413.
Haykin S. Neural networks: a comprehensive foundation. 2nd ed. New Jersey: Prentice Hall; 1999.
Jenkins WM. An introduction to neural computing for the structural engineer. Structural Engineering. 1997; 75(3):38-41.
Jolliffe IT. Principal component analysis. 2nd ed. New York: Springer; 2002.
Nafey AS. Neural network based correlation for critical heat flux in steam-water flows. International Journal of Thermal Sciences. 2009; 48(12):2264-70. http://dx.doi.org/10.1016/j.ijthermalsci.2009.04.010.
Niemi H, Bulsari A, Palosaari S. Simulation of membrane separation by neural networks. Journal of Membrane Science. 1995; 102:185-91. http://dx.doi.org/10.1016/0376-7388(94)00314-O.
Roque WL, Alberich-Bayarri A. Tortuosity influence on the trabecular bone elasticity and mechanical competence. In: Tavares JMRS, Jorge RN, editors. Developments in medical image processing and computational vision, lecture notes in computational vision and biomechanics. Cham: Springer; 2015. v. 19, p. 173-91.
Roque WL, Arcaro K, Alberich-Bayarri A. Mechanical competence of bone: a new parameter to grade trabecular bone fragility from tortuosity and elasticity. IEEE Transactions on Biomedical Engineering. 2013a; 60(5):1363-70. http://dx.doi.org/10.1109/TBME.2012.2234457. PMid:23268378.
Roque WL, Arcaro K, Alberich-Bayarri A and Tabor Z. An investigation of the mechanical competence parameter to grade the trabecular bone fragility. In: Tavares JMRS, Jorge RN, editors. Proceedings of the 4th Computational Vision and Medical Image Processing; 2013 Oct 14-16; Funchal. Leiden: CRC Press; 2013b; p. 387-92.
Christopher JJ, Ramakrishnan S. Assessment and classification of mechanical strength components of human femur trabecular bone using digital image processing and neural networks. Journal of Mechanics in Medicine and Biology. 2007; 7(3):315-24. http://dx.doi.org/10.1142/S0219519407002339.
Dimai HP, Redlich K, Peretz M, Borgström F, Siebert U, Mahlich J. Economic burden of osteoporotic fractures in Austria. Health Economics Review. 2012; 2(1):12. http://dx.doi.org/10.1186/2191-1991-2-12. PMid:22827971.
Filletti ER, Roque WL. Neural network prediction of the trabecular bone mechanical competence parameter. In: Braidot A, Hadad A, editors. IFMBE Proceedings; 2014 Oct 29-31; Paraná. Cham: Springer International Publishing; 2015. v. 49, p. 226-29.
Gregory JS, Junold RM, Undrill PE, Aspden RM. Analysis of trabecular bone structure using fourier transforms and neural networks. IEEE Transactions on Information Technology in Biomedicine. 1999; 3(4):289-94. http://dx.doi.org/10.1109/4233.809173. PMid:10719479.
Hambli R. Multiscale prediction of crack density and crack length accumulation in trabecular bone based on neural networks and nite element simulation. International Journal for Numerical Methods in Biomedical Engineering. 2011; 27(4):461-75. http://dx.doi.org/10.1002/cnm.1413.
Haykin S. Neural networks: a comprehensive foundation. 2nd ed. New Jersey: Prentice Hall; 1999.
Jenkins WM. An introduction to neural computing for the structural engineer. Structural Engineering. 1997; 75(3):38-41.
Jolliffe IT. Principal component analysis. 2nd ed. New York: Springer; 2002.
Nafey AS. Neural network based correlation for critical heat flux in steam-water flows. International Journal of Thermal Sciences. 2009; 48(12):2264-70. http://dx.doi.org/10.1016/j.ijthermalsci.2009.04.010.
Niemi H, Bulsari A, Palosaari S. Simulation of membrane separation by neural networks. Journal of Membrane Science. 1995; 102:185-91. http://dx.doi.org/10.1016/0376-7388(94)00314-O.
Roque WL, Alberich-Bayarri A. Tortuosity influence on the trabecular bone elasticity and mechanical competence. In: Tavares JMRS, Jorge RN, editors. Developments in medical image processing and computational vision, lecture notes in computational vision and biomechanics. Cham: Springer; 2015. v. 19, p. 173-91.
Roque WL, Arcaro K, Alberich-Bayarri A. Mechanical competence of bone: a new parameter to grade trabecular bone fragility from tortuosity and elasticity. IEEE Transactions on Biomedical Engineering. 2013a; 60(5):1363-70. http://dx.doi.org/10.1109/TBME.2012.2234457. PMid:23268378.
Roque WL, Arcaro K, Alberich-Bayarri A and Tabor Z. An investigation of the mechanical competence parameter to grade the trabecular bone fragility. In: Tavares JMRS, Jorge RN, editors. Proceedings of the 4th Computational Vision and Medical Image Processing; 2013 Oct 14-16; Funchal. Leiden: CRC Press; 2013b; p. 387-92.
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