Abstract
Purpose
One critical step in routine orthognathic surgery is to reestablish a desired final dental occlusion. Traditionally, the final occlusion is established by hand articulating stone dental models. To date, there are still no effective solutions to establish the final occlusion in computer-aided surgical simulation. In this study, we consider the most common one-piece maxillary orthognathic surgery and propose a three-stage approach to digitally and automatically establish the desired final dental occlusion.
Methods
The process includes three stages: (1) extraction of points of interest and teeth landmarks from a pair of upper and lower dental models; (2) establishment of Midline-Canine-Molar (M-C-M) relationship following the clinical criteria on these three regions; and (3) fine alignment of upper and lower teeth with maximum contacts without breaking the established M-C-M relationship. Our method has been quantitatively and qualitatively validated using 18 pairs of dental models.
Results
Qualitatively, experienced orthodontists assess the algorithm-articulated and hand-articulated occlusions while being blind to the methods used. They agreed that occlusion results of the two methods are equally good. Quantitatively, we measure and compare the distances between selected landmarks on upper and lower teeth for both algorithm-articulated and hand-articulated occlusions. The results showed that there was no statistically significant difference between the algorithm-articulated and hand-articulated occlusions.
Conclusion
The proposed three-stage automatic dental articulation method is able to articulate the digital dental model to the clinically desired final occlusion accurately and efficiently. It allows doctors to completely eliminate the use of stone dental models in the future.
Similar content being viewed by others
References
Xia JJ, Gateno J, Teichgraeber JF, Yuan P, Chen KC, Li J, Zhang X, Tang Z, Alfi DM (2015) Algorithm for planning a double-jaw orthognathic surgery using a computer-aided surgical simulation (CASS) protocol. Part 1: planning sequence. Int J Oral Maxillofac Surg 44(12):1431–1440. https://doi.org/10.1016/j.ijom.2015.06.006
Chabanas M, Marecaux C, Payan Y, Boutault F (2002) Models for planning and simulation in computer assisted orthognatic surgery. In: Paper presented at the proceedings of the 5th international conference on medical image computing and computer-assisted intervention-Part II
Nadjmi N, Mollemans W, Daelemans A, Van Hemelen G, Schutyser F, Berge S (2010) Virtual occlusion in planning orthognathic surgical procedures. Int J Oral Maxillofac Surg 39(5):457–462. https://doi.org/10.1016/j.ijom.2010.02.002
Wu W, Chen H, Cen Y, Hong Y, Khambay B, Heng PA (2017) Haptic simulation framework for determining virtual dental occlusion. Int J Comput Assist Radiol Surg 12(4):595–606. https://doi.org/10.1007/s11548-016-1475-3
Chang YB, Xia JJ, Gateno J, Xiong Z, Zhou X, Wong ST (2010) An automatic and robust algorithm of reestablishment of digital dental occlusion. IEEE Trans Med Imaging 29(9):1652–1663. https://doi.org/10.1109/TMI.2010.2049526
Xia JJ, Chang YB, Gateno J, Xiong Z, Zho X (2010) Automated digital dental articulation. Med Image Comput Comput Assist Interv 13(Pt 3):278–286. https://doi.org/10.1007/978-3-642-15711-0_35
Deng H, Yuan P, Wong S, Gateno J, Garrett FA, Ellis RK, English JD, Jacob HB, Kim D, Xia JJ (2019) An automatic approach to reestablish final dental occlusion for 1-piece maxillary orthognathic surgery. 2019 medical image computing and computer assisted intervention–MICCAI 2019. Springer, Cham, pp 345–353
Xia JJ, Gateno J, Teichgraeber JF, Christensen AM, Lasky RE, Lemoine JJ, Liebschner MA (2007) Accuracy of the computer-aided surgical simulation (CASS) system in the treatment of patients with complex craniomaxillofacial deformity: a pilot study. J Oral Maxillofac Surg 65(2):248–254. https://doi.org/10.1016/j.joms.2006.10.005
Hsu SS, Gateno J, Bell RB, Hirsch DL, Markiewicz MR, Teichgraeber JF, Zhou X, Xia JJ (2013) Accuracy of a computer-aided surgical simulation protocol for orthognathic surgery: a prospective multicenter study. J Oral Maxillofac Surg 71(1):128–142. https://doi.org/10.1016/j.joms.2012.03.027
Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1(8476):307–310
Lu S, Yang J, Wang W, Li Z, Lu Z (2018) Teeth classification based on extreme learning machine. In: Paper presented at the 2018 second world conference on smart trends in systems, security and sustainability (WorldS4), 30–31 Oct 2018
Kumar Y, Janardan R, Larson B (2012) Automatic feature identification in dental meshes. Comput Aided Des Appl 9(6):747–769. https://doi.org/10.3722/cadaps.2012.747-769
Kumar Y, Janardan R, Larson B, Moon J (2011) Improved segmentation of teeth in dental models. Comput Aided Des Appl 8:211–224. https://doi.org/10.3722/cadaps.2011.211-224
Wu K, Chen L, Li J, Zhou Y (2014) Tooth segmentation on dental meshes using morphologic skeleton. Comput Gr 38:199–211. https://doi.org/10.1016/j.cag.2013.10.028
Mouritsen DA (2013) Automatic segmentation of teeth in digital dental models. University of Alabama, Birmingham
Hao W, Zhongyi L (2016) Tooth separation from dental model using segmentation field. Conf Proc IEEE Eng Med Biol Soc 2016:5616–5619. https://doi.org/10.1109/embc.2016.7592000
Liao SH, Liu SJ, Zou BJ, Ding X, Liang Y, Huang JH (2015) Automatic tooth segmentation of dental mesh based on harmonic fields. Biomed Res Int 2015:187173. https://doi.org/10.1155/2015/187173
Zou BJ, Liu SJ, Liao SH, Ding X, Liang Y (2015) Interactive tooth partition of dental mesh base on tooth-target harmonic field. Comput Biol Med 56:132–144. https://doi.org/10.1016/j.compbiomed.2014.10.013
Sinthanayothin C, Tharanont W (2008) Orthodontics treatment simulation by teeth segmentation and setup. In: Paper presented at the 2008 5th international conference on electrical engineering/electronics, computer, telecommunications and information technology, 14–17 May 2008
Ma Y, Li Z (2010) Computer aided orthodontics treatment by virtual segmentation and adjustment. In: Paper presented at the 2010 international conference on image analysis and signal processing, 9–11 Apr 2010
Acknowledgment
This work was supported in part by National Institutes of Health/National Institute of Dental and Craniofacial Research grants (R01 DE022676, R01 DE027251, and R01 DE021863).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
All authors declare that they have no conflict of interest.
Ethical approval
The use of human data has been approved and performed in accordance with ethical standards (IRB# Pro00003644) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent
Informed consent was not applicable since we used historical data.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Deng, H., Yuan, P., Wong, S. et al. An automatic approach to establish clinically desired final dental occlusion for one-piece maxillary orthognathic surgery. Int J CARS 15, 1763–1773 (2020). https://doi.org/10.1007/s11548-020-02125-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-020-02125-y