Date of Award
8-2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Biomedical and Chemical Engineering and Sciences
First Advisor
Pengfei Dong
Second Advisor
Peshala Priyadarshana Thibbotuwawa Gamage
Third Advisor
Linxia Gu
Fourth Advisor
Sayed Ehsan Saghaian
Abstract
Orthodontic treatment is widely used in both adolescents and adults to correct malocclusion and improve facial aesthetics. However, treatment planning and evaluation rely on the experience of clinicians and qualitative assessment, which makes tooth movement less controllable and difficult to predict. To address this limitation, this dissertation presents a quantitative, computer-assisted biomechanical analysis framework that emphasizes localized periodontal ligament (PDL) response, rather than relying solely on traditional geometric indicators such as the center of resistance (CR). This dissertation used finite element simulations, in vitro experiments, and machine learning to investigate the center of resistance, optimize orthodontic appliances and treatment strategies, improve the control and predictability of tooth movement, and characterize the relationship between orthodontic force and PDL biomechanical responses. The dissertation addressed four objectives. Objective 1 extended the existing method for determining the center of resistance, a widely used conceptual reference point for estimating tooth movement, to make it applicable across multiple tooth morphologies. The effects of tooth morphology and force direction on the location of CR were evaluated by analyzing the tooth movement and stress distribution. Objective 2 evaluated and compared the biomechanical response of teeth and the periodontal ligament (PDL) between two maxillary segmental distalizers. It also established a relationship between the hydrostatic stress in the PDL and the load transfer on the root. Objective 3 optimized the clear aligner design to reduce the undesired incisor tipping during treatment. This optimization was investigated in terms of the tooth movement tendency and load transfer efficiency from the appliance to the tooth using both finite element simulations and in vitro experiments. Objective 4 developed a machine learning model to predict tooth movement and stress distribution in the PDL under orthodontic loading, which serves as a rapid surrogate for finite element simulation to improve the predictability of treatment planning. Collectively, this dissertation establishes a quantitative, computer-assisted biomechanical framework that links orthodontic appliance design and force transmission to tooth movement and localized PDL response, thereby providing a foundation for more predictable and efficient orthodontic treatment planning.
Recommended Citation
Wang, Jianing, "Computer-Assisted Biomechanical Analysis for Optimal Orthodontic Design" (2026). Theses and Dissertations. 1656.
https://repository.fit.edu/etd/1656