One of the unique properties of fracture healing is that bones heal by producing new tissues that eventually become indistinguishable from the original ones in the preinjury state, through the process of tissue differentiation. This process is fundamentally controlled by the mechanical conditions at the fracture site, particularly mechanical strain. Numerical models with strain-based fuzzy logic rules have been successfully applied to simulate bone healing in response to local mechanical stimuli for simple axisymmetric fracture geometries. However, these simplified models were not designed to replicate in vivo observations such as delays in healing with torsional instability or anticipated differences in healing rate between different fracture types. Accordingly, the purpose of this work was to apply fuzzy logic mechanoregulation fracture healing simulation techniques to 3D models representing a wider range of clinical fracture geometries under multi-axial loading conditions representative of clinical intramedullary nail fixation. Normalized virtual torsional rigidity of the fracture bone were used in the model to provide the structural measure to track the percentage of healing each patient had undergone.

The results of the strain-based mechnoregultaion models showed that the rate of healing depends on the geometry of the fracture, but that all fracture types experience delayed healing with torsional instability. When simulating healing with clinically relevant torsional loading and fixator mechanics, published strain-based rules for tissue destruction predicted nonunions that would not be expected clinically. This suggested that clinical fracture healing may be more robust to distortional strain than has been previously reported and that fuzzy logic models may require parameter tuning to correctly capture clinically relevant healing. Ultimately, this study is the first-ever model to include both fracture morphology effects and realistic implant mechanics and the proposed improved methods have the potential to extend into clinical fracture healing prediction.