Classification of the skeletal variation in normal occlusion.
The aims of this study were to classify normal occlusion samples into specific skeletal types and to analyze the dentoalveolar compensation in a normal occlusion in order to provide the clinically applicable differential diagnostic criteria for an individual malocclusion patient. Lateral cephalograms of 294 normal occlusion samples, who were selected from 15,836 adults through a community dental health survey, were measured. Using a principal component analysis, two factors representing the anteroposterior and vertical skeletal relationships were extracted from 18 skeletal variables. Cluster analysis was then used to classify the skeletal patterns into nine types. Nine types of polygonal charts with a profilogram were created. Discriminant analysis with a stepwise entry of variables was designed to identify several potential variables for skeletal typing, which could be linked with computerized cephalometric analysis for an individual malocclusion patient. Discriminant analysis assigned 87.8% classification accuracy to the predictive model. It was concluded that because the range of a normal occlusion includes quite diverse anteroposterior and vertical skeletal relationships, classifying the skeletal pattern and establishing an individual dentoalveolar treatment objective might facilitate clinical practice.
Kim
Ji-Young
JY
Department of Orthodontics, College of Dentistry, Seoul National University, 28-22 Yunkeun-Dong, Chongro-Ku, Seoul 110-744, South Korea.
Lee
Shin-Jae
SJ
Kim
Tae-Woo
TW
Nahm
Dong-Seok
DS
Chang
Young-Ii
YI
D000328:Adult; D002508:Cephalometry / Q000379:methods*; D016000:Cluster Analysis; D003753:Dental Health Surveys; D003768:Dental Occlusion, Centric; D016002:Discriminant Analysis; D005147:Facial Bones / Q000033:anatomy & histology*; D005260:Female; D006801:Humans; D008297:Male; D008310:Malocclusion / Q000145:classification* / Q000175:diagnosis; D025341:Principal Component Analysis