Abstract
Purpose
Preoperative planning of maxillary anterior dental implant is a prerequisite to ensuring that the implant achieves the proper three-dimensional (3D) pose, which is essential for its long-term stability. However, the current planning process is labor-intensive and subjective, relying heavily on the surgeon's experience. Consequently, this paper proposes an automatic method for computing the optimal pose of the dental implant.
Methods
The method adopts the principle of prosthetically guided dental implant placement. Initially, the prosthesis coordinate system is established to determine the implant candidate orientations. Subsequently, virtual slices of the maxilla in the buccal–palatal direction are generated according to the prosthesis position. By extracting feature points from the virtual slices, the implant candidate starting points are acquired. Then, a candidate pose set is obtained by combining these candidate starting points and orientations. Finally, a pose evaluation indicator is introduced to determine the optimal implant pose from this set.
Results
Twenty-two cases were utilized to validate the method. The results show that the method could determine an ideal pose for the dental implant, with the average minimum distance between the implant and the left tooth root, the right tooth root, the palatal side, and the buccal side being \(2.57\pm 0.53\) mm, \(2.59\pm 0.65\) mm, \(0.74\pm 0.19\) mm, \(1.83\pm 0.16\) mm, respectively. The planning time was less than 9 s.
Conclusion
Unlike manual planning, the proposed method can efficiently and accurately complete maxillary anterior dental implant planning, providing a theoretical analysis of the success rate of the implant. Thus, it has great potential for future clinical application.
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References
Testori T, Weinstein T, Scutellà F, Wang HL, Zucchelli G (2018) Implant placement in the esthetic area: criteria for positioning single and multiple implants. Periodontol 2000 77(1):176–196
Correa LR, Spin-Neto R, Stavropoulos A, Schropp L, da Silveira HED, Wenzel A (2014) Planning of dental implant size with digital panoramic radiographs, CBCT-generated panoramic images, and CBCT cross-sectional images. Clin Oral Implant Res 25(6):690–695
Chen X, Yuan J, Wang C, Huang Y, Kang L (2010) Modular preoperative planning software for computer-aided oral implantology and the application of a novel stereolithographic template: a pilot study. Clin Implant Dent Relat Res 12(3):181–193
Greenberg AM (2015) Digital technologies for dental implant treatment planning and guided surgery. Oral Maxillofac Surg Clin 27(2):319–340
Worthington P, Rubenstein J, Hatcher DC (2010) The role of cone-beam computed tomography in the planning and placement of implants. J Am Dent Assoc 141:19S-24S
Xu J, Wang S, Zhou Z, Liu J, Jiang X, Chen X (2020) Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net. Int J Comput Assist Radiol Surg 15:1457–1465
Xu J, Liu J, Zhang D, Zhou Z, Jiang X, Zhang C, Chen X (2021) Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates. Int J Comput Assist Radiol Surg 16:1785–1794
Pankert T, Lee H, Peters F, Hölzle F, Modabber A, Raith S (2023) Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network. Int J Comput Assist Radiol Surg 18:1–10
Cui Z, Fang Y, Mei L, Zhang B, Yu B, Liu J, Jiang C, Sun Y, Ma L, Huang J, Liu Y, Zhao Y, Lian C, Ding Z, Zhu M, Shen D (2022) A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. Nat Commun 13(1):2096
Kurt Bayrakdar S, Orhan K, Bayrakdar IS, Bilgir E, Ezhov M, Gusarev M, Shumilov E (2021) A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med Imaging 21(1):86
Bodhe, R., Sivakumar, S., & Raghuwanshi, A. (2022). Design and development of deep learning approach for dental implant planning. In: 2022 International Conference on Green Energy, Computing and Sustainable Technology (GECOST) (pp. 269–274).
Galanis CC, Sfantsikopoulos MM, Koidis PT, Kafantaris NM, Mpikos PG (2007) Computer methods for automating preoperative dental implant planning: implant positioning and size assignment. Comput Methods Progr Biomed 86(1):30–38
Sadrimanesh R, Siadat H, Sadr-Eshkevari P, Monzavi A, Maurer P, Rashad A (2012) Alveolar bone stress around implants with different abutment angulation: an FE-analysis of anterior maxilla. Implant Dent 21(3):196–201
Buser D, Martin W, Belser UC (2004) Optimizing esthetics for implant restorations in the anterior maxilla: anatomic and surgical considerations. Int J Oral Maxillofac Implants 19(7):43–61
Yoda N, Zheng K, Chen J, Li W, Swain M, Sasaki K, Li Q (2017) Bone morphological effects on post-implantation remodeling of maxillary anterior buccal bone: a clinical and biomechanical study. J Prosthodont Res 61(4):393–402
Feng Y, Fan J, Tao B, Wang S, Mo J, Wu Y, Liang Q, Chen X (2022) An image-guided hybrid robot system for dental implant surgery. Int J Comput Assist Radiol Surg 17(1):15–26
Raabe C, Biel P, Dulla FA, Janner SF, Abou-Ayash S, Couso-Queiruga E (2023) Inter-and intraindividual variability in virtual single-tooth implant positioning. Clin Oral Implant Res 00:1–11
Acknowledgments
This work was supported by grants from National Key R&D Program of China (2017YFB1302901).
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This study was approved by the ethics committee of the Ninth People’s Hospital Affiliated with Shanghai Jiao Tong University, School of Medicine, Shanghai, China (SH9H-2022-T181-1), and was conducted according to the Helsinki Declaration of 1964, as revised in 2008.
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Feng, Y., Tao, B., Fan, J. et al. Automatic planning of maxillary anterior dental implant based on prosthetically guided and pose evaluation indicator. Int J CARS (2024). https://doi.org/10.1007/s11548-024-03142-x
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DOI: https://doi.org/10.1007/s11548-024-03142-x