Universidade de Pernambuco - UPE, School of Dentistry, Department of Oral and Maxillofacial Surgery and Traumatology, Recife, PE, Brazil.
Faculdade de Odontologia do Recife - FOR, Recife Dentistry College, Department of Oral and Maxillofacial Radiology, Recife, PE, Brazil.
Braz Oral Res. 2021 Mar 15;35:e034. doi: 10.1590/1807-3107bor-2021.vol35.0034. eCollection 2021.
The objective of this study was to apply elliptic Fourier analysis (EFA) to find shape differences among skeletal growth patterns in both radiographic and tomographic panoramic views, controlling for asymmetry. Lateral and panoramic images were obtained from 350 patients. After screening patients with asymmetric linear and angular values and natural asymmetric hemimandibular shape, 240 patients were included in the study: 48 with tomographic information and 192 with radiographic information. The images were classified according to the mandibular plane angle and the ANB angle. Mandibular contours were digitized on the panoramic images and EFA was performed with 20 harmonics, filtering rotation, translation and size properties. As there were no differences between radiographic and tomographic panoramic mandibular contours and normal distribution was found in all groups, MANOVA was conducted to determine differences using a Hotelling's p-values with Bonferroni correction and an XY graph tool was applied to visualize these differences graphically. A 95% confidence level was used. Significative differences were found among hypodivergent, normodivergent, and hyperdivergent patterns in Class I, II, and III (p < 0.05), located mainly in the symphyseal region. The results of this study suggest that EFA is a useful tool to mathematically analyze mandibular contours and their morphological differences given by facial biotypes. This method could improve the precision of the mandibular prediction models.
本研究旨在应用椭圆傅里叶分析(EFA)来发现放射和断层全景视图中骨骼生长模式的形状差异,同时控制不对称性。从 350 名患者中获得了侧位和全景图像。在筛选出具有不对称线性和角度值以及自然不对称半下颌形状的患者后,共有 240 名患者纳入研究:48 名患者有断层信息,192 名患者有放射信息。根据下颌平面角和 ANB 角对图像进行分类。在全景图像上对下颌轮廓进行数字化,并使用 20 个谐波进行 EFA,过滤旋转、平移和大小属性。由于放射和断层全景下颌轮廓之间没有差异,并且所有组均呈正态分布,因此使用 MANOVA 进行差异确定,使用 Hotelling 的 p 值进行检验,并应用 XY 图形工具直观地可视化这些差异。置信水平为 95%。在 I 类、II 类和 III 类中,低角型、均角型和高角型之间存在显著差异(p < 0.05),主要位于正中联合区。本研究结果表明,EFA 是一种有用的工具,可以从面部生物型的角度对下颌轮廓及其形态差异进行数学分析。这种方法可以提高下颌预测模型的精度。