A credible homogenized finite element model to predict radius fracture in the case of a forward fall.

Fragility fractures that occur after a fall from a standing height or less are almost always due to osteoporosis, which remains underdiagnosed and untreated. Patient-specific finite element (FE) models have been introduced to predict bone strength and strain. This approach, based on structure mechanics, is derived from Quantitative Computed Tomography (QCT), and element mechanical properties are computed from bone mineral densities. In this study, we developed a credible finite element model of the radius to discriminate low-trauma-fractured radii from non-fractured radii obtained experimentally. Thirty cadaveric radii were impacted with the same loading condition at 2 m/s, and experimental surface strain was retrieved by stereo-correlation in addition to failure loads in fracture cases. Finite element models of the distal radius were created from clinical computed tomography. Different density-elasticity relationships and failure criteria were tested. The strongest agreement (simulations-experiments) for average strain showed a Spearman's rank correlation (ρ) between 0.75 and 0.82, p < 0.0001, with a root mean square error between 0.14 and 0.19%. The experimental mean strain was 0.55%. Predicted failure load error (23%) was minimized for derived Pistoia's failure criterion. Numerical failure demonstrated area under the receiver operating characteristic (ROC) curves of 0.76 when classifying radius fractures with an accuracy of 82%. These results suggest that a credible FE modelling method in a large region of interest (distal radius) is a suitable technique to predict radius fractures after a forward fall.

Variabilities in µQCT-based FEA of a tumoral bone mice model.

A finite element analysis based on Micro-Quantitative Computed Tomography (µQCT) is a method with high potential to improve fracture risk prediction. However, the segmentation process and model generation are generally not automatized in their entirety. Even with a rigorous protocol, the operator might add uncertainties during the creation of the model. The aim of this study was to evaluate a µQCT-based model of mice tumoral and sham tibias in terms of the variabilities induced by the operator and sensitivity to operator-dependent variables (such as model orientation or length). Two different operators generated finite element (FE) models from µCT images of 8 female Balb/c nude mice tibias aged 10 weeks old with bone tumors induced in the right tibia and with sham injection in the left. From these models, predicted failure load was determined for two different boundary conditions: fixed support and spherical joints. The difference between the predicted and experimental failure load of both operators was large (-122% to 93%). The difference in the predicted failure load between operators was less for the spherical joints boundary conditions (9.8%) than for the fixed support (58.3%), p < 0.001, whereas varying the orientation of bone tibia caused more variability for the fixed support boundary condition (44.7%) than for the spherical joints (9.1%), p < 0.002. Varying tibia length had no significant effect, regardless of boundary conditions (<4%). When using the same mesh and same orientation, the difference between operators is non-significant (<6%) for each model. This study showed that the operator influences the failure load assessed by a µQCT-based finite element model of the tumoral and sham mice tibias. The results suggest that automation is needed for better reproducibility.