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Jing du penn state rating11/7/2023 9 Some studies have compared the overall bone density or bone morphology without considering their spatial distribution. In some studies, the comparison was qualitative and was carried out visually. To validate these models, the computed results have been compared with optical images, radiographs, or computed tomography images. Our prior work on the numerical simulation of mandible bone remodeling under tooth loading has demonstrated the effects of several model parameters on the bone density distribution at the equilibrium state, and has also discussed the stability, uniqueness, and convergence in the models. These models are iterative, nonlinear, and multi-parameter. These cycles persist until the mechanical stimulus returns to the equilibrium range. When the mechanical stimulus shifts outside the equilibrium range, it leads to an increase or decrease in bone density which again influences the mechanical stimulus. In these models, when the mechanical stimulus remains in an equilibrium range, bone density remains unchanged. 2, 3 Later, the algorithms were extended to dental problems, such as dental implant material selection, implant geometry design, or just mandibular bone density distribution around teeth. Initially, these algorithms were developed for orthopedic applications, especially for femoral heads. 1 There have been several numerical algorithms that have simulated this process. The results provide a new method to compare the results of adaptive bone remodeling simulation with experimental data, and also provide model parameters to predict the bone density distribution surrounding a dental implant that replaced the tooth.īone adapts to changes of mechanical stimulus by remodeling activities, which results in changes in bone density. The bending and torsion moments on the sagittal section of the mandible resulted in lower bone density near the center than those towards the edge of the mandible. The bite forces were transmitted through tooth roots to the surrounding bone, thus stimulating high trabecular bone density near the roots. The results exhibited close agreement with a coefficient of correlation of 0.8499. Linear regression analysis was performed between the bone density computed by numerical simulation and that obtained from image analysis, for every trabecular bone element. A bone remodeling algorithm was implemented to compute the bone density distribution at equilibrium. Strain energy density in the bone under normal chewing and biting forces was computed using finite element analysis. Cone beam computed tomography (CBCT) images of multiple human subjects were superimposed to obtain a continuous bone density spatial distribution map inside the mandible supporting the lateral incisor. The results of numerical simulation of mechanically adaptive bone remodeling have been compared with clinical images.
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