Are Subject-Specific Musculoskeletal Models Robust to the Uncertainties in Parameter Identification?

Abstract

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 Influence of Component Alignment and Ligament Properties on Tibiofemoral Contact Forces in Total Knee Replacement

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Sensitivity of a juvenile subject-specific musculoskeletal model of the ankle joint to the variability of operator-dependent input

“…4 Despite efforts to automate these procedures, 23,24 this is typically conducted by a human operator and is thus liable to unavoidable inter- and intra-operator variability in the parameters defined.…”

Stochastic multiscale modelling of cortical bone elasticity based on high-resolution imaging

“…However, at the present, in vivo medical images turn out to be too much blurred for extracting accurate information on bone microstructure. Uncertainties affecting available in vivo information on bone microstructure may question the reliability of results obtained by multiscale models using these data as entry (Valente et al 2014). As long as input data are uncertain, deterministic approaches may become unreliable and new modelling paradigms are required.…”

“…Modelling uncertainties and computing stochastic responses may be useful to assess robustness of subject-specific predictive models in musculoskeletal biomechanics (Valente et al 2014). Aiming at characterising the mechanical properties of bone in vivo, both hierarchical structure of bone and the uncertainties existing on experimental data should account for.…”