Kishi S
Yurinoki Orthodontics Information Center, Tokyo, Japan.
J Oral Sci. 1999 Sep;41(3):111-5. doi: 10.2334/josnusd.41.111.
Lundström et al. proposed a proportional analysis system for the soft tissue facial profile in the natural head position. To use this method for further epidemiological investigation and to interpret the characteristics of this analysis, each measurement (index) was identified in comparison to the other indices using cluster and factor analyses. Facial profiles of 111 (mean age: 22.9 years) Japanese males were measured and 11 indices (8 horizontal, 2 vertical and 1 horizontal/vertical) were calculated. Almost all internal co-relationships between each index were statistically significant (p < 0.05, 0.01). Variable cluster analysis classified indices into four major clusters and clarified the attributes of the 11 indices. The first cluster was index No. 1, 2, 3 and 7. The second cluster was index No. 6. The 3rd cluster was index No. 4, 5, 11, 8 and 10. The 4th cluster was index No. 9. These clusters are thought as vertical facial balance, upper and lower jaw relation or horizontal/vertical balance, chin morphology, and horizontal facial balance. From factor analysis, three factor axes that explained the characteristics of 11 indices were found (accumulated contribution rate: 76.5%). The heaviest loading factor was index No. 1,2 (0.95) on axis I, 5 (0.83) on axis II and 6 (0.78) on axis III. Therefore, axis I, axis II and axis III are thought to be based on the position of the soft tissue Nasion, SLI and Pogonion, respectively. Common indices which are included in both analyses are thought to be valid as a clue to reduce the number of measurement parameters.
伦德斯特伦等人提出了一种针对自然头位下软组织面部轮廓的比例分析系统。为了将此方法用于进一步的流行病学调查并解读该分析的特征,通过聚类分析和因子分析,将每个测量值(指标)与其他指标进行比较来确定。对111名(平均年龄:22.9岁)日本男性的面部轮廓进行了测量,并计算了11个指标(8个水平指标、2个垂直指标和1个水平/垂直指标)。每个指标之间几乎所有的内部相互关系都具有统计学意义(p < 0.05,0.01)。变量聚类分析将指标分为四个主要类别,并阐明了这11个指标的属性。第一类是指标1、2、3和7。第二类是指标6。第三类是指标4、5、11、8和10。第四类是指标9。这些类别被认为分别代表垂直面部平衡、上下颌关系或水平/垂直平衡、下巴形态以及水平面部平衡。通过因子分析,发现了三个解释11个指标特征的因子轴(累积贡献率:76.5%)。在轴I上载荷最大的因子是指标1、2(0.95),在轴II上是指标5(0.83),在轴III上是指标6(0.78)。因此,轴I、轴II和轴III分别被认为是基于软组织鼻根点、鼻唇角和颏前点的位置。两种分析中都包含的共同指标被认为是有效的线索,可用于减少测量参数的数量。