Rauber Cedric, Lüscher Dominique, Poux Lucile, Schori Maria, Deml Moritz C, Hasler Carol-Claudius, Bassani Tito, Galbusera Fabio, Büchler Philippe, Schmid Stefan
Spinal Movement Biomechanics Group, School of Health Professions, Bern University of Applied Sciences, Bern, Switzerland; Computational Bioengineering Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Spinal Movement Biomechanics Group, School of Health Professions, Bern University of Applied Sciences, Bern, Switzerland.
J Biomech. 2024 Jan;163:111922. doi: 10.1016/j.jbiomech.2023.111922. Epub 2024 Jan 6.
Musculoskeletal (MSK) models offer great potential for predicting the muscle forces required to inform more detailed simulations of vertebral endplate loading in adolescent idiopathic scoliosis (AIS). In this work, simulations based on static optimization were compared with in vivo measurements in two AIS patients to determine whether computational approaches alone are sufficient for accurate prediction of paraspinal muscle activity during functional activities. We used biplanar radiographs and marker-based motion capture, ground reaction force, and electromyography (EMG) data from two patients with mild and moderate thoracolumbar AIS (Cobb angles: 21° and 45°, respectively) during standing while holding two weights in front (reference position), walking, running, and object lifting. Using a fully automated approach, 3D spinal shape was extracted from the radiographs. Geometrically personalized OpenSim-based MSK models were created by deforming the spine of pre-scaled full-body models of children/adolescents. Simulations were performed using an experimentally controlled backward approach. Differences between model predictions and EMG measurements of paraspinal muscle activity (both expressed as a percentage of the reference position values) at three different locations around the scoliotic main curve were quantified by root mean square error (RMSE) and cross-correlation (XCorr). Predicted and measured muscle activity correlated best for mild AIS during object lifting (XCorr's ≥ 0.97), with relatively low RMSE values. For moderate AIS as well as the walking and running activities, agreement was lower, with XCorr reaching values of 0.51 and comparably high RMSE values. This study demonstrates that static optimization alone seems not appropriate for predicting muscle activity in AIS patients, particularly in those with more than mild deformations as well as when performing upright activities such as walking and running.
肌肉骨骼(MSK)模型在预测青少年特发性脊柱侧凸(AIS)中椎体终板负荷的更详细模拟所需的肌肉力量方面具有巨大潜力。在这项研究中,将基于静态优化的模拟与两名AIS患者的体内测量结果进行了比较,以确定仅靠计算方法是否足以准确预测功能活动期间椎旁肌的活动。我们使用了双平面X线片、基于标记的运动捕捉、地面反作用力以及两名轻度和中度胸腰段AIS患者(Cobb角分别为21°和45°)在站立时双手持重物于身前(参考位置)、行走、跑步和举物时的肌电图(EMG)数据。采用全自动方法从X线片中提取三维脊柱形状。通过对儿童/青少年预缩放全身模型的脊柱进行变形,创建了基于几何个性化的OpenSim MSK模型。使用实验控制的反向方法进行模拟。通过均方根误差(RMSE)和互相关(XCorr)对脊柱侧弯主曲线周围三个不同位置的椎旁肌活动的模型预测值与EMG测量值之间的差异(均表示为参考位置值的百分比)进行量化。在举物过程中,轻度AIS患者的预测和测量肌肉活动相关性最佳(XCorr≥0.97),RMSE值相对较低。对于中度AIS以及行走和跑步活动,一致性较低,XCorr值达到0.51,RMSE值相对较高。这项研究表明,仅靠静态优化似乎不适用于预测AIS患者的肌肉活动,特别是对于那些畸形程度超过轻度的患者以及在进行行走和跑步等直立活动时。