Mixed dentition analysis using a multivariate approach

Seung-Hyun Seo; Hongseok An; Shin-Jae Lee; Won Hee Lim; Bong-Rae Kim

doi:10.4041/kjod.2009.39.2.112

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Seo, An, Lee, Lim, and Kim: Mixed dentition analysis using a multivariate approach

Original Article

Korean Journal of Orthodontics

Korean Journal of Orthodontics 2009; 39(2): 112-119.

Published online: 30 April 2009

DOI: https://doi.org/10.4041/kjod.2009.39.2.112

Mixed dentition analysis using a multivariate approach

Seung-Hyun Seo, BS^a, Hongseok An, BS, DDS^b, Shin-Jae Lee, DDS, MSD, PhD^c, Won Hee Lim, DDS, MS, PhD^d, Bong-Rae Kim, BA, MS, PhD^e

^aGraduate student, School of Dentistry, Seoul National University, Korea.

^bPublic Health Dentist, Korea.

^cAssociate Professor, School of Dentistry, Seoul National University, Korea.

^dAssistant Professor, School of Dentistry, Seoul National University, Korea.

^eBK Assistant Professor, School of Dentistry, Seoul National University, Korea.

Corresponding author: Shin-Jae Lee. Department of Orthodontics, School of Dentistry, Seoul National University, 28, Yeongeon-dong, Jongno-gu, Seoul 110-749, Korea. +82 2 2072 3019; nonext@snu.ac.kr

Received 25 September 2008 Revised 12 January 2009 Accepted 16 January 2009

Abstract

Objective

To develop a mixed dentition analysis method in consideration of the normal variation of tooth sizes.

Methods

According to the tooth-size of the maxillary central incisor, maxillary 1st molar, mandibular central incisor, mandibular lateral incisor, and mandibular 1st molar, 307 normal occlusion subjects were clustered into the smaller and larger tooth-size groups. Multiple regression analyses were then performed to predict the sizes of the canine and premolars for the 2 groups and both genders separately. For a cross validation dataset, 504 malocclusion patients were assigned into the 2 groups. Then multiple regression equations were applied.

Results

Our results show that the maximum errors of the predicted space for the canine, 1st and 2nd premolars were 0.71 and 0.82 mm residual standard deviation for the normal occlusion and malocclusion groups, respectively. For malocclusion patients, the prediction errors did not imply a statistically significant difference depending on the types of malocclusion nor the types of tooth-size groups. The frequency of prediction error more than 1 mm and 2 mm were 17.3% and 1.8%, respectively. The overall prediction accuracy was dramatically improved in this study compared to that of previous studies.

Conclusions

The computer aided calculation method used in this study appeared to be more efficient.

Keywords: Tooth size prediction, Cluster analysis, Discriminant analysis, Multiple regression

Figures and Tables

Table 1

Previous reports regarding mixed dentition analysis for various ethnic populations

Normal, Normal occlusion subjects; Patients, subjects who visited orthodontists; NA, not available.

Table 2

Classification function coefficients which are used to cluster and then reassign the subjects to 2 groups (smaller vs. larger group)

Table 3

Multiple regression coefficients to predict the sum of canine, 1^st premolar, and 2^nd premolar sizes

Mx345, Sum of maxillary canine; 1^st premolar, and 2^nd premolar size Mn345, sum of mandibular canine, 1^st premolar, and 2^nd premolar size. Statistical significance of the regression coefficients ^*p < 0.05; ^†p < 0.01.

Table 4

Residual (predicted value - real value) analysis after multiple regression to predict the sum of canine, 1^st premolar, and 2^nd premolar using maxillary central incisor, maxillary 1^st molar, mandibular central incisor, mandibular lateral incisor, and mandibular molar

RSD, Residual standard deviation.

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