ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

Figure 4: Absolute prediction errors for Mises stresses and for the magnitude of
displacement for two patients: Top row (patient 0001) 0.02 ± 0.37 MPa, -0.014 ± 0.023
mm, bottom row (patient 0002) 0.07 ± 0.27 MPa, -0.062 ± 0.033mm.
5. DISCUSSION
The model fails to predict the bones response if only shape and material properties are
considered. Hence, a statistical mechanical model, which includes bone shape, material
properties and boundary conditions, is required for reliable predictions. By
incorporating mechanical parameters, the model is able to predict Mises stress with
error of 0.5 MPa which is small compared to the failure strength of bone.
Some prediction outliers can be observed at nodes with high forces (e.g. muscle
insertion point). This could be optimized by a FE model which distributes the loads over
an area instead of a single node. Additionally, since the raw CT images were not
calibrated the model's capacity for predictions, for example fracture risks, is currently
limited.
However, we were able to show the power of our method to avoid additional FE
calculations by combining SMA, FE and a regression model to predict FE outcomes.
The accuracy of this methods is already suitable for several clinical applications.