Liu H Y, Ge Z P, Niu S Q, Song Y, Yu Y L
School of Stomatology of Qingdao University, Qingdao 266003, China.
Department of Oral and Maxillofacial Surgery, Qingdao Stomatological Hospital, Qingdao 266001, China.
Zhonghua Kou Qiang Yi Xue Za Zhi. 2020 Feb 9;55(2):98-103. doi: 10.3760/cma.j.issn.1002-0098.2020.02.005.
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. 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 ((2)) 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. Multiple linear regression equation for age estimation based on two-dimensional linear indexes was as follows: Y=-113.928+33.743×e(AH)(3)(/100)+58.844×e(PH)(4)(/100)+20.590×e(AP)(4)(/100)( "e" was a natural constant, e≈2.718), (2)=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×e(A)H(3)/PH(3)+3.227×e(A)H(3)/AP(3)+2.149×e(A)H(3)/H(3)+1.961×e(A)H(4)/H(4), (2)=0.576, SEE=1.70. Multiple linear regression equation of age estimation by the volume index was as follows: Y=-16.828+22.184×e(V)(3)(/10 000), (2)=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 ((2)) 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. 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.
分析9至17岁女性安氏Ⅰ类骨骼患者年龄与第三、四颈椎锥形束CT(CBCT)图像之间的相关性,并建立一种计算年龄的定量评估方法。收集2017年9月至2019年3月期间青岛口腔医院108例9至17岁女性安氏Ⅰ类骨骼患者的CBCT图像。测量第三颈椎(C3)和第四颈椎(C4)的二维线性值(AH:椎体前缘高度;H:椎体中部高度;PH:椎体后缘高度;AP:椎体宽度)、二维线性比值(AH/PH、AH/AP、AH/H、H/AP、H/PH、PH/AP)以及三维体积值。通过测量值的指数变换和多元线性回归分析,筛选出评估年龄的最佳指标,并比较多元线性回归方程((2))的拟合度和年龄估计的准确性(SEE)。2019年4月至2019年7月期间从青岛口腔医院补充收集27例9至17岁女性安氏Ⅰ类骨骼患者的CBCT图像,以此验证回归方程的准确性。基于二维线性指标的年龄估计多元线性回归方程如下:Y = -113.928 + 33.743×e(AH)(3)(/100) + 58.844×e(PH)(4)(/100) + 20.590×e(AP)(4)(/100)(“e”为自然常数,e≈2.718),(2) = 0.745,SEE = 1.31。基于二维线性比值指标的年龄估计多元线性回归方程如下:Y = -0.076 - 2.284×e(A)H(3)/PH(3) + 3.227×e(A)H(3)/AP(3) + 2.149×e(A)H(3)/H(3) + 1.961×e(A)H(4)/H(4),(2) = 0.576,SEE = 1.70。通过体积指标进行年龄估计的多元线性回归方程如下:Y = -16.828 + 22.184×e(V)(3)(/10 000),(2) = 0.555,SEE = 1.71。对27例新患者的数据进行检验。C3和C4椎体的CBCT测量指标推断出年龄估计方程的拟合度((2))和准确性(SEE)。二维线性值优于二维线性比值,二维线性比值优于三维体积值。它们的估计标准误差分别为1.74、2.00和2.37。C3和C4的CBCT图像二维线性指标可用于估计9至17岁女性安氏Ⅰ类骨骼患者的年龄,且该方法的准确性高于二维比值指标和三维体积指标。