Hannah Iain, Montefiori Erica, Modenese Luca, Prinold Joe, Viceconti Marco, Mazzà Claudia
1 INSIGNEO Institute for in silico Medicine, University of Sheffield, Sheffield, UK.
2 Department of Mechanical Engineering, University of Sheffield, Sheffield, UK.
Proc Inst Mech Eng H. 2017 May;231(5):415-422. doi: 10.1177/0954411917701167.
Subject-specific musculoskeletal modelling is especially useful in the study of juvenile and pathological subjects. However, such methodologies typically require a human operator to identify key landmarks from medical imaging data and are thus affected by unavoidable variability in the parameters defined and subsequent model predictions. The aim of this study was to thus quantify the inter- and intra-operator repeatability of a subject-specific modelling methodology developed for the analysis of subjects with juvenile idiopathic arthritis. Three operators each created subject-specific musculoskeletal foot and ankle models via palpation of bony landmarks, adjustment of geometrical muscle points and definition of joint coordinate systems. These models were then fused to a generic Arnold lower limb model for each of three modelled patients. The repeatability of each modelling operation was found to be comparable to those previously reported for the modelling of healthy, adult subjects. However, the inter-operator repeatability of muscle point definition was significantly greater than intra-operator repeatability ( p < 0.05) and predicted ankle joint contact forces ranged by up to 24% and 10% of the peak force for the inter- and intra-operator analyses, respectively. Similarly, the maximum inter- and intra-operator variations in muscle force output were 64% and 23% of peak force, respectively. Our results suggest that subject-specific modelling is operator dependent at the foot and ankle, with the definition of muscle geometry the most significant source of output uncertainty. The development of automated procedures to prevent the misplacement of crucial muscle points should therefore be considered a particular priority for those developing subject-specific models.
特定个体的肌肉骨骼建模在青少年和病理受试者的研究中特别有用。然而,此类方法通常需要人工操作员从医学影像数据中识别关键标志点,因此会受到所定义参数以及后续模型预测中不可避免的变异性影响。本研究的目的是量化为分析青少年特发性关节炎受试者而开发的特定个体建模方法在不同操作员之间以及同一操作员内部的可重复性。三名操作员分别通过触诊骨标志点、调整几何肌肉点和定义关节坐标系,创建了特定个体的足部和踝关节肌肉骨骼模型。然后,将这些模型与三名建模患者各自的通用阿诺德下肢模型进行融合。发现每个建模操作的可重复性与先前报道的健康成年受试者建模的可重复性相当。然而,肌肉点定义在不同操作员之间的可重复性显著高于同一操作员内部的可重复性(p < 0.05),并且在不同操作员之间和同一操作员内部分析中,预测的踝关节接触力分别高达峰值力的24%和10%。同样,肌肉力输出在不同操作员之间和同一操作员内部的最大变化分别为峰值力的64%和23%。我们的结果表明,足部和踝关节的特定个体建模依赖于操作员,肌肉几何形状的定义是输出不确定性的最重要来源。因此,对于开发特定个体模型的人员来说,开发防止关键肌肉点错位的自动化程序应被视为特别优先事项。