Bern University of Applied Sciences, Department of Health Professions, Division of Physiotherapy, Spinal Movement Biomechanics Group, Bern, Switzerland; University of Basel, Faculty of Medicine, Basel, Switzerland.
University of Zurich, Balgrist University Hospital, Department of Chiropractic Medicine, Integrative Spinal Research, Zurich, Switzerland; University of Zurich, Zurich, Switzerland; ETH Zurich, Department of Health Science and Technology, Institute for Biomechanics, Zurich, Switzerland.
J Biomech. 2022 May;137:111102. doi: 10.1016/j.jbiomech.2022.111102. Epub 2022 Apr 25.
Musculoskeletal models have the potential to improve diagnosis and optimize clinical treatment by predicting accurate outcomes on an individual basis. However, the subject-specific modeling of spinal alignment is often strongly simplified or is based on radiographic assessments, exposing subjects to unnecessary radiation. We therefore developed and introduced a novel skin marker-based approach for modeling subject-specific spinal alignment and evaluated its feasibility by comparing the predicted L1/L2 spinal loads during various functional activities with the loads predicted by the generically scaled models as well as with in vivo measured data obtained from the OrthoLoad database. Spinal loading simulations resulted in considerably higher compressive forces for both scaling approaches over all simulated activities, and AP shear forces that were closer or similar to the in vivo data for the subject-specific approach during upright standing activities and for the generic approach during activities that involved large flexions. These results underline the feasibility of the proposed method and associated workflow for inter- and intra-subject investigations using musculoskeletal simulations. When implemented into standard model scaling workflows, it is expected to improve the accuracy of muscle activity and joint loading simulations, which is crucial for investigations of treatment effects or pathology-dependent deviations.
肌肉骨骼模型有可能通过预测个体的准确结果来改善诊断和优化临床治疗。然而,脊柱排列的特定于个体的建模通常被强烈简化,或者基于射线照相评估,使患者暴露于不必要的辐射下。因此,我们开发并引入了一种新的基于皮肤标记的方法来对特定于个体的脊柱排列进行建模,并通过将在各种功能活动期间预测的 L1/L2 脊柱载荷与通用比例模型预测的载荷以及来自 OrthoLoad 数据库的体内测量数据进行比较,评估了其可行性。脊柱加载模拟导致两种缩放方法在所有模拟活动中都产生了明显更高的压缩力,并且在直立站立活动中,特定于个体的方法的前向剪切力更接近或类似于体内数据,而在涉及大弯曲的活动中,通用方法的前向剪切力更接近或类似于体内数据。这些结果强调了使用肌肉骨骼模拟进行跨个体和个体内研究的所提出方法和相关工作流程的可行性。当将其实施到标准模型缩放工作流程中时,预计可以提高肌肉活动和关节载荷模拟的准确性,这对于研究治疗效果或病理相关性偏差至关重要。