Effects of Daily Mastication on Bone Remodeling With Implant-Tooth-Supported Fixed Partial Prosthesis: A Finite Element Study

Abstract

Implant-tooth-supported fixed dentures are designed clinically due to the limitation of the local anatomy in specific implant sites or following an implant failure. However, it is difficult to precisely simulate the post-surgical biomechanical behavior on alveolar bone tissue with daily mastication. This paper presents and validates a reliable system to predict alveolar bone material properties and to identify the best fit treatment. We randomly chose 23 patients who had been followed for at least three years after Implant-tooth-supported fixed dentures. A poroelastic finite element model (FEM) is designed to characterize the biomechanical stresses of the aortic tissue for each patient. The Kernel least mean square is used to model the relationship between the alveolar bone material properties and biomechanical stress features generated by the daily occlusal force. Therefore, the alveolar bone material properties and the optimal treatment can be predicted by this integrated model prior to the operation for a new patient. The prediction accuracy of our model is 91.5%, more accurate than linear finite element model-LFEM (72.8%), mass-spring model-MSM (78.5%), and mass tensor model-MTM (80.2%). That demonstrates the high value of this model as a decision-making assistant for surgical planning of the patients who are scheduled to undergo dentures. The results of this paper demonstrate that this integrated model can predict alveolar bone material properties postoperatively with high accuracy. It combines bio-mechanical and machine learning approach to create a surgical planning tool which may support the clinical decision in the future.

Keywords

• Implant-tooth-supported fixed dentures
• alveolar bone tissue
• stress
• poroelastic
• KLMS