Bayome Mohamed, Han Seong Ho, Choi Jong-Hyuk, Kim Seong-Hun, Baek Seung-Hak, Kim Dong-Jae, Kook Yoon-Ah
Department of Orthodontics, Medical School, The Catholic University of Korea, Seoul, Korea.
Aust Orthod J. 2011 Nov;27(2):117-24.
The purpose of the present study was to use facial axis (FA) points to classify dental arch form generated from an analysis of 3-D virtual models of a sample of normal occlusions. A secondary aim was to introduce a new arch form template based on this classification for clinical application.
One hundred and twenty five plaster models of Class I occlusions were 3-D scanned (Orapix Co., Ltd, Seoul, Korea) and FA points digitized on the virtual models using Rapidform 2006 software (INUS Technology Inc., Seoul, Korea). Following intercanine and intermolar arch width and depth measurements, K-means cluster analysis was applied on 77 cases (Dataset 1) to classify the sample into arch form types. A curve of best fit of the mean arch form of each type was generated. The remaining 48 cases (Dataset 2) were mapped into the clusters and a multivariate test was performed to assess the differences among the clusters.
Classification into five clusters demonstrated maximum inter-cluster distance in the arch parameters and produced the most homogeneous cluster size. The differences between the 5 cluster types were statistically but not clinically significant and so they were recombined to form three clusters representing 'narrow', 'moderate' and 'wide' arch forms.
A template with three arch form types based on anterior and posterior dimensions has been proposed through 3-D analysis of FA points for more accurate arch form identification and arch wire selection.
本研究的目的是利用面部轴(FA)点对从正常咬合样本的三维虚拟模型分析中生成的牙弓形态进行分类。第二个目的是基于这种分类引入一种新的牙弓形态模板以供临床应用。
对125个I类咬合的石膏模型进行三维扫描(韩国首尔Orapix有限公司),并使用Rapidform 2006软件(韩国首尔INUS技术公司)在虚拟模型上对FA点进行数字化处理。在测量犬间和磨牙间牙弓宽度和深度之后,对77例病例(数据集1)应用K均值聚类分析将样本分类为牙弓形态类型。生成了每种类型平均牙弓形态的最佳拟合曲线。将其余48例病例(数据集2)映射到聚类中,并进行多变量检验以评估聚类之间的差异。
分类为五个聚类显示出牙弓参数中的最大类间距离,并产生了最均匀的聚类大小。5种聚类类型之间的差异在统计学上有意义,但在临床上无意义,因此将它们重新组合形成代表“窄”、“中”和“宽”牙弓形态的三个聚类。
通过对FA点进行三维分析,提出了一种基于前后维度的具有三种牙弓形态类型的模板,以实现更准确的牙弓形态识别和弓丝选择。