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Finding standard dental arch forms from a nationwide standard occlusion study using a Gaussian functional mixture model

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Abstract

Orthodontists are interested in finding a set of standard arch forms for clinical orthodontic practice. In this paper, we propose a functional clustering method for the dental arches based on a mixture of U-shaped curves. We decide the number of clusters (equivalently, mixture components) using the Bayesian information criterion and the jump criterion based on a given distortion function. We apply our method to clustering the dental arch data from the nationwide standard occlusion study conducted in Korea from 1997 to 2005. The data are composed of dental arches of 306 subjects with normal occlusion selected from 15,836 young adults. We also provide the comparison of the proposed method to other existing methods.

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References

  • Angle, E. H. (1899). Classification of malocclusion. Dental Cosmos, 41, 248–264.

    Google Scholar 

  • Begole, E. (1980). Application of the cubic spline function in the description of dental arch form. Journal of Dental Research, 59, 1549–1556.

    Article  Google Scholar 

  • Biggerstaff, R. H. (1972). Three variations in dental arch form estimated by a quadratic equation. Journal of Dental Research, 51, 1509.

    Article  Google Scholar 

  • Bozdogan, H., & Sclove, S. L. (1984). Multi-sample cluster analysis using Akaike’s information criterion. Annals of the Institute of Statistical Mathematics, 36, 163–180.

    Article  Google Scholar 

  • Burdi, A. R. (1968). Morphogenesis of mandibular dental arch shape in human embryos. Journal of Dental Research, 47, 50–58.

    Article  Google Scholar 

  • Camporesi, M., Franchi, L., Baccetti, T., & Antonini, A. (2006). Thin-plate spline analysis of arch form in a Southern European popoulation with an ideal natrual occlusion. European Journal of Orthodontics, 28, 135–140.

    Article  Google Scholar 

  • Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13, 195–212.

    Article  MathSciNet  Google Scholar 

  • Currier, J. (1969). A computerized geometric analysis of human dental arch form. American Journal of Orthodontics, 56, 164–179.

    Article  Google Scholar 

  • Dasgupta, A., & Raftery, A. E. (1998). Detecting features in spatial point processes with clutter via model-based clustering. Journal of the American Statistical Association, 93, 294–302.

    Article  Google Scholar 

  • Dryden, I. L., & Mardia, K. V. (1998). Statistical shape analysis. John Wiley & Sons.

    MATH  Google Scholar 

  • Fraley, C., & Raftery, A. E. (1998). How many clusters? Which clustering method? Answers via model-based cluster analysis. The Computer Journal, 41, 578–588.

    Article  Google Scholar 

  • Gu, Q., Shibata, T., Fujita, K., & Takada, K. (2002). Application of vector quantization algorithm to dental arch classification in orthodontics practice. In Proceedings of IEEE world automation congress (pp. 279–285).

    Google Scholar 

  • Hechter, F. (1978). Symmetry and dental arch form of orthodontically treated patients. Dental Journal, 44, 173–184.

    Google Scholar 

  • Henrikson, J., Persson, M., & Thilander, B. (2001). Long-term stability of dental arch form in normal occlusion from 13 to 31 years of age. European Journal of Orthodontics, 23, 51–61.

    Article  Google Scholar 

  • James, G. M., & Sugar, C. A. (2003). Clustering for sparsely sampled functional data. Journal of the American Statistical Association, 98, 397–408.

    Article  MathSciNet  Google Scholar 

  • Lee, S. J., Lee, S., Lim, J., Park, H. J., & Wheeler, T. (2011). Method to classify human dental arch form. American Journal of Orthodontics and Dentofacial Orthopedics, 140(1), 87–96.

    Article  Google Scholar 

  • Lim, J., Lee, S., Park, H. J., Lee, K. E., & Lee, S. J. (2014). Bootstrap method to evaluate tightness of clusters with application to the Korean standard occlusion study. Journal of Statistical Computation and Simulation, 84, 360–372.

    Article  MathSciNet  Google Scholar 

  • MacConail, M. A., & Scher, E. A. (1949). Ideal form of the human dental arcade with some prosthetic applications. Journal of Dental Research, 69, 285–302.

    Google Scholar 

  • Mutinelli, S., Cozzani, M., Manfredi, M., Bee, M., & Siciliani, G. (2008). Dental arch changes following rapid maxillary expansion. European Journal of Orthodontics, 30, 469–476.

    Article  Google Scholar 

  • Noroozi, H., Nik, T., & Saeeda, R. (2001). The dental arch form revisited. The Angle Orthodontist, 71, 386–389.

    Google Scholar 

  • Pepe, S. (1975). Polynomial and catenary curve fits to human dental arches. Journal of Dental Research, 36, 996–1003.

    Google Scholar 

  • Ramsay, J. (1988). Monotone regression splines in action. Statistical Science, 3, 425–441.

    Article  Google Scholar 

  • Ronay, V., Miner, R., Will, L., & Arai, K. (2008). Mandibular arch form: the relationship between dental and basal anatomy. American Journal of Orthodontics and Dentofacial Orthopedics, 134, 430–438.

    Article  Google Scholar 

  • Sampson, P. (1981). Dental arch shape: A statistical analysis using conic sections. American Journal of Orthodontics, 79, 535–548.

    Article  Google Scholar 

  • Schoenemann, P. H., & Carroll, R. (1970). Fitting one matrix to another under choice of a central dilation and a rigid motion. Psychometrika, 35(2), 245–255.

    Article  Google Scholar 

  • Scott, J. H. (1957). The shape of dental arches. Journal of Dental Research, 36, 996–1003.

    Article  Google Scholar 

  • Sugar, C. A., & James, G. M. (2003). Finding the number of clusters in a dataset: An information-theoretic approach. Journal of the American Statistical Association, 98, 750–762.

    Article  MathSciNet  Google Scholar 

  • Taner, T., Ciger, S., El, H., Germec, D., & Es, A. (2004). Evaluation of dental arch width and form changes after orthodontic treatment and retention with a new computerized method. American Journal of Orthodontics and Dentofacial Orthopedics, 126, 464–475.

    Article  Google Scholar 

  • Trivino, T., Siqueira, D., & Scanavini, M. (2008). A new concept of mandibular dental arch forms with normal occlusion. American Journal of Orthodontics and Dentofacial Orthopedics, 133(1), 10.e15–10.e22.

    Article  Google Scholar 

  • Valenzuela, A., Pardo, M., & Yezioro, S. (2002). Description of dental arch form using the Fourier series. The International Journal of Adult Orthodontics and Orthognathic Surgery, 17, 59–65.

    Google Scholar 

  • Yun, Y. K., Kook, Y., Kim, S., Mo, S., Cha, K., Kim, J., et al. (2004). Mandibular clinical arch forms in Koreans with normal occlusions. Korean Journal of Orthodontics, 34, 481–487.

    Google Scholar 

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Correspondence to Sungim Lee.

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Lee, K.E., Lim, J., Won, JH. et al. Finding standard dental arch forms from a nationwide standard occlusion study using a Gaussian functional mixture model. J. Korean Stat. Soc. 44, 477–489 (2015). https://doi.org/10.1016/j.jkss.2015.04.003

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  • DOI: https://doi.org/10.1016/j.jkss.2015.04.003

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