Kim Ji-Young, Lee Shin-Jae, Kim Tae-Woo, Nahm Dong-Seok, Chang Young-Ii
Department of Orthodontics, College of Dentistry, Seoul National University, 28-22 Yunkeun-Dong, Chongro-Ku, Seoul 110-744, South Korea.
Angle Orthod. 2005 May;75(3):311-9. doi: 10.1043/0003-3219(2005)75[311:COTSVI]2.0.CO;2.
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.
本研究的目的是将正常咬合样本分类为特定的骨骼类型，并分析正常咬合中的牙牙槽代偿，以便为个体错牙合患者提供临床适用的鉴别诊断标准。对通过社区口腔健康调查从15836名成年人中选取的294例正常咬合样本的头颅侧位片进行了测量。使用主成分分析，从18个骨骼变量中提取了代表前后和垂直骨骼关系的两个因素。然后使用聚类分析将骨骼模式分为九种类型。创建了九种带有侧面轮廓图的多边形图表。设计了变量逐步进入的判别分析，以识别骨骼分型的几个潜在变量，这些变量可与个体错牙合患者的计算机化头影测量分析相联系。判别分析赋予预测模型87.8%的分类准确率。得出的结论是，由于正常咬合的范围包括相当多样的前后和垂直骨骼关系，对骨骼模式进行分类并建立个体牙牙槽治疗目标可能会促进临床实践。
