Abstract
This study aims to predict the range of motion (ROM) with secondary parameters such as the intra-disc pressure (IDP) and facet force under complex physiological loading for Anterior Cervical Discectomy and Fusion (ACDF) and degeneration occurring at various functional spine units (FSU) in a human cervical spine, using machine learning models. Multi-target regression is a machine learning (ML) algorithm that outputs an array of values for a given set of input parameters. An anatomically accurate and validated finite element model (FEM) of a human sub-axial spinal column (C2-T1) was used in this study. Material properties for all spine components were taken from literature. An algorithm programmed using Python was interfaced with ABAQUS to automate the calculation of ROM, IDP and facet force generated from the nodal and elemental data of an intact model, a model with ACDF at the C5-C6 level and a model with mild degeneration at C5-C6 separately. The data generated from the FEA results were trained with random forest regression, support vector machines and multiple linear regression algorithms. The results indicated that the R2 value was significantly high for the random forest regression model, accounting for a very less Root Mean Square Error (RMSE) and was able to predict more than one target variable unlike the rest. Conclusively, the target variables were predicted under complex loading conditions for clinical conditions of fusion and degeneration with high accuracy and less computational cost.
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References
Panjabi, M.M., Cholewicki, J., Nibu, K., et al.: Criticial load of the human cervical spine: an in vitro experimental study. Clin. Biomech. 13, 11–17 (1998)
Yoganandan, N., Kumaresan, S., Pintar, F.A.: ‘Biomechanics of the cervical spine. Part 2. Cervical spine soft tissue responses and biomechanical modeling.’ Clin. Biomech. 16(1), 1–27 (2001)
Merali Zamir, G. et al.: Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy. PloS one 14(4), e0215133 (2019). https://doi.org/10.1371/journal.pone.0215133
Gandhi, A., Grosland, N., Kallemeyn, N., Kode, S., Fredericks, D., Smucker, J.: Biomechanical analysis of the cervical spine following disc degeneration, disc fusion, and disc replacement: a finite element study. Int. J. Spine Surgery 13, 6066. https://doi.org/10.14444/6066
Assietti, R., Beretta, F., Arienta, C.: Two-level anterior cervical discectomy and cageassisted fusion without plates. Neurosurg. Focus 12(1), 3 (2002)
Kumaresan, S., Yoganandan, N., Pintar, F.A., Maiman, D.J., Kuppa, S.: Biomechanical study of pediatric human cervical spine: a finite element approach. J. Biomech. Eng. 122(1), 60–71
Reilly, D.T., Burstein, A.H.: The elastic and ultimate properties of compact bone tissue. J Biomech 8(6), 393–405 (1975)
Kopperdahl, D.L., Keaveny, T.M.: Yield strain behavior of trabecular bone. J. Biomech. 31(7), 601–8
Yoganandan, N., Pintar, F.A., Stemper, B.D., Baisden, J.L., Aktay, R., Shender, B.S., Paskoff, G., Laud, P.: Trabecular bone density of male human cervical and lumbar vertebrae. Bone. 39(2), 336–44 (2006)
Toosizadeh, N., Haghpanahi, M.: Generating a finite element model of the cervical spine: Estimating muscle forces and internal loads. Scientia Iranica. 18, 1237–1245 (2011). https://doi.org/10.1016/j.scient.2011.10.002
Panzer, M.B., Cronin, D.S.: C4–C5 segment finite element model development, validation, and load-sharing investigation. J. Biomech. 42, 480–490 (2009)
Yamada, H.: Strength of Biological Materials. Williams and Wilkins (1970)
Mattucci, S.F.E., Moulton, J.A., Chandrashekar, N., Cronin, D.S.: Strain rate dependent properties of younger human cervical spine ligaments. J. Mech. Behav. Biomed. Mater. 10, 216–226 (2012)
Yoganandan, N., Kumaresan, S., Pintar, F.A.: Geometric and mechanical properties of human cervical spine ligaments. J. Biomech. Eng. 122, 623 (2000)
Tetreault, L., Palubiski, L.M., Kryshtalskyj, M., Idler, R.K., Martin, A.R., Ganau, M., et al.: Significant predictors of outcome following surgery for the treatment of degenerative cervical Myelopathy: a systematic review of the literature. Neurosurg. Clin. N. Am. 29(115–27), e35 (2018). https://doi.org/10.1016/j.nec.2017.09.020
Choi, H., Purushothaman, Y., Baisden, J., Yoganandan, N.: Unique biomechanical signatures of Bryan, Prodisc C, and Prestige LP cervical disc replacements: a finite element modelling study. Eur. Spine J. (2019)
Wheeldon, J.A., Pintar, F.A., Knowles, S., Yoganandan, N.: Experimental flexion/extension data corridors for validation of finite element models of the young, normal cervical spine. J. Biomech. 39(2), 375–380 (2006)
Bell, K.M., Debski, R.E., Sowa, G.A., Kang, J.D., Tashman, S.: (2019) Optimization of compressive loading parameters to mimic in vivo cervical spine kinematics in vitro. J. Biomech. 18(87), 107–113 (2019)
Patel, V.V., Wuthrich, Z.R., McGilvray, K.C., Lafeur, M.C., Lindley, E.M., Sun, D., Puttlitz, C.M.: Cervical facet force analysis after disc replacement versus fusion. Clin. Biomech. (Bristol, Avon) 44, 52–58 (2017)
Sun G., Li, S., Cao, Y., Lang, F.: Cervical Cancer diagnosis based on random forest. Int. J. Perform. Eng. 13, 446–457. https://doi.org/10.23940/ijpe.17.04.p12.446457.
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This study was supported by the Dassault Systemes Foundation, India; The Department of Neurosurgery ,Medical College of Wisconsin, USA.
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Dilip Kumar, S., Shruthi, R., Deepak, R., Davidson Jebaseelan, D., Babu, L., Yoganandan, N. (2021). Predictive Biomechanical Study on the Human Cervical Spine Under Complex Physiological Loading. In: Lim, C.T., Leo, H.L., Yeow, R. (eds) 17th International Conference on Biomedical Engineering. ICBME 2019. IFMBE Proceedings, vol 79. Springer, Cham. https://doi.org/10.1007/978-3-030-62045-5_11
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