Correlation between cone-beam CT images of the third and fourth cervical vertebrae and age in female skeletal class Ⅰ patients aged between 9 and 17 years in Qingdao
10.3760/cma.j.issn.1002-0098.2020.02.005
- VernacularTitle: 青岛市9~17岁女性骨性Ⅰ类错第三和第四颈椎锥形束CT影像特征与年龄的相关性分析
- Author:
Hengyu LIU
1
;
Zhipu GE
2
;
Shuqiang NIU
3
;
Yu SONG
3
;
Yanling YU
3
Author Information
1. School of Stomatology of Qingdao University, Qingdao 266003, China
2. Department of Oral and Maxillofacial Surgery, Qingdao Stomatological Hospital, Qingdao 266001, China
3. Department of Orthodontics, Qingdao Stomatological Hospital, Qingdao 266001, China
- Publication Type:Journal Article
- Keywords:
Cone-beam computed tomography;
Cervical vertebrae;
Malocclusion, Angle class Ⅰ;
Regression analysis
- From:Chinese Journal of Stomatology
2020;55(2):98-103
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the correlation between the age and the cone-beam CT (CBCT) images of the third and fourth cervical vertebrae in female skeletal class Ⅰ patients aged between 9 and 17 years, and to establish a quantitative evaluation method for calculating the age.
Methods:CBCT images of 108 female skeletal class Ⅰ patients aged between 9 and 17 years were collected from Qingdao Stomatological Hospital from September, 2017 to March, 2019. The two-dimensional linear values (AH: height of anterior edge of vertebral body; H: height of middle part of vertebral body; PH: height of posterior edge of vertebral body; AP: width of vertebral body), the two-dimensional linear ratio values (AH/PH, AH/AP, AH/H, H/AP, H/PH, PH/AP) and the three-dimensional volume values of the third vertical vertebrae (C3) and the fourth vertical vertebrae (C4) were measured. By Exponential transformation of measurements and multiple linear regression analysis, the optimal index for evaluating age were screened, and the fitting degree of multiple linear regression equation (R2) and the accuracy of age estimation (SEE) were compared. CBCT images of 27 female skeletal class Ⅰ patients aged from 9 to 17 years were added from Qingdao Stomatological Hospital between April, 2019 and July, 2019, by which the accuracy of the regression equation was verified.
Results:Multiple linear regression equation for age estimation based on two-dimensional linear indexes was as follows: Y=-113.928+33.743×eAH3/100+58.844×ePH4/100+20.590×eAP4/100( "e" was a natural constant, e≈2.718), R2=0.745, SEE=1.31. Multiple linear regression equation for age estimation based on two-dimensional linear ratio indexes was as follows: Y=-0.076-2.284×eAH3/PH3+3.227×eAH3/AP3+2.149×eAH3/H3+1.961×eAH4/H4, R2=0.576, SEE=1.70. Multiple linear regression equation of age estimation by the volume index was as follows: Y=-16.828+22.184×eV3/10 000, R2=0.555, SEE=1.71. The data of 27 new patients were tested. The CBCT measurement index of C3 and C4 vertebral bodies inferred the fitting degree (R2) and accuracy (SEE) of the equation of the age estimation. The two-dimensional linear value was superior to the two-dimensional linear ratio and the latter was superior to the three-dimensional volume value. The standard error of the estimate about them was 1.74, 2.00 and 2.37, respectively.
Conclusions:The two-dimensional linear index of CBCT images of C3 and C4 could be used to estimate the age of 9 to 17-year-old female skeletal class Ⅰ patients, and the accuracy of the method was higher than that of two-dimensional ratio index and three-dimensional volume index.
