Finite element modeling of the lumbar spine has advanced significantly in the last decade [1] and become a relatively well established method for examining fundamental biomechanics as well as new spinal implants and procedures. However, most of these models only represent a single subject and do not account for normal subject-to-subject variation. This limitation can be addressed using a probabilistic simulation in which virtual specimens are used to represent a broad population of subjects. The greatest challenge to implementing probabilistic techniques in biomechanical simulation is parameterization of anatomy to capture normal variation across subjects. In the present study, shape variation was captured using a statistical shape model (SSM) and implemented in a probabilistic framework to evaluate biomechanics of a single motion segment. The Monte Carlo (MC) method is a common probabilistic simulation technique that is robust even for non-monotonic or highly non-linear systems. The purpose of this study was to perform a probabilistic study of a lumbar motion segment using MC simulation to determine the sensitivity of spinal rotations to changes in geometry and soft tissue material properties.

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