Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312) across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force predictions could be affected by an uncertainty in the same order of magnitude of its value, although this condition has low probability to occur.

The shape of the proximal femur has been demonstrated to be important in the occurrence of fractures of the femoral neck. Unfortunately, multiple geometric measurements frequently used to describe this shape are highly correlated. A new method, active shape modeling (ASM) has been developed to quantify the morphology of the femur. This describes the shape in terms of orthogonal modes of variation that, consequently, are all independent. To test this method, digitized standard pelvic radiographs were obtained from 26 women who had suffered a hip fracture and compared with images from 24 age-matched controls with no fracture. All subjects also had their bone mineral density (BMD) measured at five sites using dual-energy X-ray absorptiometry. An ASM was developed and principal components analysis used to identify the modes which best described the shape. Discriminant analysis was used to determine which variable, or combination of variables, was best able to discriminate between the groups. ASM alone correctly identified 74% of the individuals and placed them in the appropriate group. Only one of the BMD values (Ward's triangle) achieved a higher value (82%). A combination of Ward's triangle BMD and ASM improved the accuracy to 90%. Geometric variables used in this study were weaker, correctly classifying less than 60% of the study group. Logistic regression showed that after adjustment for age, body mass index, and BMD, the ASM data was still independently associated with hip fracture (odds ratio (OR)=1.83, 95% confidence interval 1.08 to 3.11). The odds ratio was calculated relative to a 10% increase in the probability of belonging to the fracture group. Though these initial results were obtained from a limited data set, this study shows that ASM may be a powerful method to help identify individuals at risk of a hip fracture in the future.

Bone mineral density (BMD) is generally used to predict the risk of fracture in osteoporotic subjects. However, femoral neck BMD and spine BMD have been reported not to be significantly different among patients with hip or vertebral fractures, suggesting that other risk factors are needed to determine the different fracture types. Proximal femur geometry (PFG) parameters, such as hip axis length (HAL), femoral neck-shaft angle (NSA) and femoral neck diameter (FND) have also been shown to predict the risk of hip fracture. These parameters are statistically different in spine fractures compared with both types of hip fractures (trochanteric and femoral neck) when considered together. We wanted to assess the difference in these parameters by comparing spine fractures with a homogeneous group of hip fractures, i.e. femoral neck fractures. 807 post-menopausal women were divided into three groups; those with vertebral fractures (182), those with femoral neck fractures (134) and a control group without fractures (491). Dual X-ray absorptiometry (DXA) scans of the spine and hip were carried out to measure BMD and define the PFG parameters of the hip. Data were statistically analysed. In agreement with other authors, we found that women with femoral neck fractures had longer HAL, wider FND and larger NSA than controls, whereas there were no statistically significant differences in PFG between women with spine fractures and controls. Logistic regression showed HAL and NSA could predict the risk of femoral neck but not vertebral fracture. These data indicate specificity of some PFG parameters for hip fracture risk.