Angle Orthod. 2019 Sep;89(5):768-774. doi: 10.2319/100518-717.1. Epub 2019 Mar 21.
To identify the most characteristic variables out of a large number of anatomic landmark variables on three-dimensional computed tomography (CT) images. A modified principal component analysis (PCA) was used to identify which anatomic structures would demonstrate the major variabilities that would most characterize the patient.
Data were collected from 217 patients with severe skeletal Class III malocclusions who had undergone orthognathic surgery. The input variables were composed of a total of 740 variables consisting of three-dimensional Cartesian coordinates and their Euclidean distances of 104 soft tissue and 81 hard tissue landmarks identified on the CT images. A statistical method, a modified PCA based on the penalized matrix decomposition, was performed to extract the principal components.
The first 10 (8 soft tissue, 2 hard tissue) principal components from the 740 input variables explained 63% of the total variance. The most conspicuous principal components indicated that groups of soft tissue variables on the nose, lips, and eyes explained more variability than skeletal variables did. In other words, these soft tissue components were most representative of the differences among the Class III patients.
On three-dimensional images, soft tissues had more variability than the skeletal anatomic structures. In the assessment of three-dimensional facial variability, a limited number of anatomic landmarks being used today did not seem sufficient. Nevertheless, this modified PCA may be used to analyze orthodontic three-dimensional images in the future, but it may not fully express the variability of the patients.
从三维 CT 图像上大量解剖标志变量中识别出最具特征的变量。采用改进的主成分分析（PCA）来确定哪些解剖结构将表现出最大的变异性，从而最能描述患者的特征。
从 217 名接受正颌手术的严重骨骼 III 类错畸形患者中收集数据。输入变量由总共 740 个变量组成，包括三维笛卡尔坐标及其在 CT 图像上识别的 104 个软组织和 81 个硬组织标志的欧几里得距离。采用基于惩罚矩阵分解的改进 PCA 进行统计分析，以提取主成分。
从 740 个输入变量中提取的前 10 个（8 个硬组织，2 个软组织）主成分解释了总方差的 63%。最显著的主成分表明，鼻子、嘴唇和眼睛上的软组织变量组比骨骼变量组具有更大的变异性。换句话说，这些软组织成分最能代表 III 类患者之间的差异。
在三维图像上，软组织的变异性大于骨骼解剖结构。在评估三维面部变异性时，目前使用的少数解剖标志似乎不够充分。然而，这种改进的 PCA 可用于分析未来的正畸三维图像，但可能无法完全表达患者的变异性。
