Gould Samuele L, Davico Giorgio, Palanca Marco, Viceconti Marco, Cristofolini Luca
Department of Industrial Engineering, Alma Mater Studiorum-University of Bologna, Bologna, Italy.
Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
Front Bioeng Biotechnol. 2024 Jul 22;12:1304334. doi: 10.3389/fbioe.2024.1304334. eCollection 2024.
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.
通过预测模拟，多体模型可通过确定最佳手术程序来辅助脊柱疾病的治疗。实现准确预测的关键在于椎间关节的定义。关节位姿通常通过虚拟触诊来定义。椎间关节刚度要么从文献中获取，要么使用优化方法计算特定标本的刚度。本研究测试了一种用于确定特定标本刚度的优化方法的可行性，并研究了指定关节位姿对个体特异性估计刚度的影响。此外，还研究了关节位姿和刚度对预测运动准确性的影响。创建了基于计算机断层扫描的腰椎节段模型。关节由从可能的笛卡尔空间中用拉丁超立方技术采样的虚拟触诊地标定义。使用一种优化方法为500个模型确定特定标本的刚度。通过对每个成功优化的模型针对十种不同刚度进行正向动态模拟，进行了双因素分析。优化计算出了大范围的刚度，表明优化后的特定标本刚度对指定的关节位姿和相关不确定性高度敏感。有限数量的优化关节刚度和关节位姿组合能够准确预测运动学。双因素分析表明，在所探索的范围内，关节位姿定义比刚度更重要。为了获得低于1毫米和1°的运动学预测误差以及合适的特定标本刚度，用于关节定义的虚拟触诊地标的精度应优于2.9毫米。
