Mixed dentition analysis using a multivariate approach.
10.4041/kjod.2009.39.2.112
- Author:
Seung Hyun SEO
1
;
Hongseok AN
;
Shin Jae LEE
;
Won Hee LIM
;
Bong Rae KIM
Author Information
1. Graduate student, School of Dentistry, Seoul National University, Korea.
- Publication Type:Original Article
- Keywords:
Tooth size prediction;
Cluster analysis;
Discriminant analysis;
Multiple regression
- MeSH:
Bicuspid;
Cluster Analysis;
Dentition, Mixed;
Discriminant Analysis;
Humans;
Incisor;
Malocclusion;
Molar;
Tooth
- From:Korean Journal of Orthodontics
2009;39(2):112-119
- CountryRepublic of Korea
- Language:Korean
-
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.
