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The aim of this paper is to develop a multiscale hierarchical hybrid modelbased on finite element analysis and neural network computation to linkmesoscopic scale (trabecular network level) and macroscopic (whole bone level)to simulate bone remodelling process. Because whole bone simulation consideringthe 3D trabecular level is time consuming, the finite element calculation isperformed at macroscopic level and a trained neural network are employed asnumerical devices for substituting the finite element code needed for themesoscale prediction. The bone mechanical properties are updated at macroscopicscale depending on the morphological organization at the mesoscopic computed bythe trained neural network. The digital image-based modeling technique usingm-CT and voxel finite element mesh is used to capture 2 mm3 RepresentativeVolume Elements at mesoscale level in a femur head. The input data for theartificial neural network are a set of bone material parameters, boundaryconditions and the applied stress. The output data is the updated boneproperties and some trabecular bone factors. The presented approach, to ourknowledge, is the first model incorporating both FE analysis and neural networkcomputation to simulate the multilevel bone adaptation in rapid way.

Multiscale approach for bone remodeling simulation based on finite element and neural network computation