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Updated: Aug 23, 2026

Precision Measurements and Parametric Models of Vertebral Endplates
Published on: September 17, 2019
Probabilistic design analysis of the influence of material property on the human cervical spine
1School of Mechanical and Production Engineering, Nanyang Technological University, Singapore. hongwan@pmail.ntu.edu.sg
Abstract:
Studies reported previously in the literature have described the importance of material variation on the cervical responses and have examined some effects by varying the material properties, but there is no systematic approach using statistical methods to understand the influence of material variation on a cervical spine model under a full range of loading conditions, especially under compression and anterior and posterior shear. A probabilistic design system based on Monte Carlo simulation methods using Latin hypercube sampling techniques is used to analyze the material sensitivity of a C4-C6 cervical spine model involving 13 uncertain input parameters on the biomechanical responses and disc annulus stresses under compression, anterior shear, posterior shear, flexion, extension, lateral bending, and axial rotation. The loading types and range of values were as follows: compression, 0-1 mm; anterior shear, 0-2 mm; posterior shear, 0-3.5 mm; flexion, extension, lateral bending, and axial rotation. 0-1.8 Nm with 73.6-N preload. For each case, the load-deflection and key stress values at various spinal components were captured after each load step. The model was also validated under the same conditions. The minimum and maximum predicted responses were within the range of the experimental data. Ignoring compression loading, the combined effects on the biomechanical responses of the cervical ligaments under the remaining loads are enormous. Their total impacts are almost equal to or slightly less than the influence of disc annulus. Results show that the fiber mechanical properties did not have a significant effect on the compressive stiffness. This study reveals important features that help us identify the critical input parameters and enable us to reduce the development time of a patient-specific biomechanical model.
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