Identification of a lumped-parameter model of the intervertebral joint from experimental data

Front Bioeng Biotechnol. 2024 Jul 22:12:1304334. doi: 10.3389/fbioe.2024.1304334. eCollection 2024.

Abstract

Through predictive simulations, multibody models can aid the treatment of spinal pathologies by identifying optimal surgical procedures. Critical to achieving accurate predictions is the definition of the intervertebral joint. The joint pose is often defined by virtual palpation. Intervertebral joint stiffnesses are either derived from literature, or specimen-specific stiffnesses are calculated with optimisation methods. This study tested the feasibility of an optimisation method for determining the specimen-specific stiffnesses and investigated the influence of the assigned joint pose on the subject-specific estimated stiffness. Furthermore, the influence of the joint pose and the stiffness on the accuracy of the predicted motion was investigated. A computed tomography based model of a lumbar spine segment was created. Joints were defined from virtually palpated landmarks sampled with a Latin Hypercube technique from a possible Cartesian space. An optimisation method was used to determine specimen-specific stiffnesses for 500 models. A two-factor analysis was performed by running forward dynamic simulations for ten different stiffnesses for each successfully optimised model. The optimisations calculated a large range of stiffnesses, indicating the optimised specimen-specific stiffnesses were highly sensitive to the assigned joint pose and related uncertainties. A limited number of combinations of optimised joint stiffnesses and joint poses could accurately predict the kinematics. The two-factor analysis indicated that, for the ranges explored, the joint pose definition was more important than the stiffness. To obtain kinematic prediction errors below 1 mm and 1° and suitable specimen-specific stiffnesses the precision of virtually palpated landmarks for joint definition should be better than 2.9 mm.

Keywords: intervertebral joint; multibody modelling; musculoskeletal modelling; personalisation; sensitivity; specimen-specific; stiffness.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by: the HEU H2022 project “Metastra - Computer-Aided Effective Fracture Risk Stratification Of Patients With Vertebral Metastases For Personalised Treatment Through Robust Computational Models Validated In Clinical Settings” (topic HLTH-2022-12-01, grant ID 101080135), AOSpine Discovery and Innovation Awards (AOSDIA 2019_063_TUM_MP), the EU H2020 project “Mobilise-D: Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement” (topic IMI2-2017-13-07, grant ID 820820), the EU H2020 project “In Silico World: Lowering barriers to ubiquitous adoption of In Silico Trials” (grant ID 101016503).