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A computational approach to automated dental implant placement: Bone density optimization around the implant with the
Hong-Tzong Yau1, Guan-Yu Shen2, Hien Vu-Dinh3
1Distinguished Professor, Department of Mechanical Engineering, Advanced Institute of Manufacturing with High-tech Innovations/AIM-HI, National Chung Cheng University, Chiayi, Taiwan; and Professor, School of Dentistry Kaohsiung, Medical University Kaohsiung, Kaohsiung, Taiwan, ROC.
Statement Of Problem:
Dental implants have been a prevalent method of restoring lost teeth and providing dental function, with recent advancements driven by computer-guided techniques. However, limitations persist concerning the evaluation of anatomic and prosthetic factors, which demand extensive expertise and manual effort.
Purpose:
The purpose of this study was to evaluate an implant placement system capable of automatically suggesting optimal implant positions by enhancing stability by maximizing bone density surrounding the implant.
Material And Methods:
A comprehensive 3-dimensional (3D) jaw model, achieved by integrating and aligning cone beam computed tomography (CBCT) and intraoral scans was constructed. Following this, automatic constraint searches were performed to define the positional boundaries of the implant by considering surrounding structures such as the prosthesis, alveolar bone, and adjacent teeth. The Levenberg-Marquardt algorithm (LMA) was used to optimize bone density in Hounsfield unit (HU) values around the implant by incorporating penalty terms to enforce constraint conditions.
Results:
The proposed approach successfully identified optimal implant positions within the most robust bone regions while maintaining functional and esthetic considerations as guided by the constraints. The feasibility of this method was validated through 2 patient treatments: a single-tooth implant (the mandibular left second molar) and a multiple-tooth implant (the mandibular right second premolar and first molar). The postimplantation visual outcomes and the convergence of the optimized parameters demonstrated the system's effectiveness in suggesting the optimal implant position.
Conclusions:
The proposed system effectively determined implant positions using the LMA, ensuring both mechanical stability and optimal esthetics. Furthermore, the fully automated process significantly reduced the time and reliance on manual expertise while maintaining high accuracy.
