Reliable and streamlined model setup for digital twin assessment of fracture healing

In large animal models of bone fracture repair, postmortem torsional testing is commonly used to assess healing biomechanics. Bending and axial tests are physiologically relevant, but much less commonly performed. Virtual torsional testing using image-based finite element models has been validated to postmortem bench tests, but its predictive value for capturing whole-bone mechanics and fracture healing quality under other physiologically relevant loading modes has not yet been established. Accordingly, the purpose of this study was to evaluate the association between mechanical biomarkers derived from virtual torsion, axial, and bending tests under strict alignment and malalignment conditions. Computed tomography (CT) scans from 24 intact and operated sheep tibiae and 29 human tibial fractures were used to create digital twins that were subjected to torsion, axial, and bending tests. The results indicated that torsional rigidity is a strong surrogate for bending flexural rigidity in both ovine and human bones. Torsional rigidity and axial stiffness were strongly correlated in the ovine data, but only moderately in human fractures due to the complex fracture patterns. Axial testing was highly prone to stiffness estimation errors as high as 50% if the applied load and anatomic axis were not perfectly aligned. In contrast, torsional rigidity had errors <1.3% for all malalignment scenarios. Based on this study, virtual torsional rigidity is the recommended summary mechanical biomarker of bone healing because it captures variations in healing biomechanics that are present in other loading modes with a simple setup that is insensitive to alignment error.