基于遗传算法的骨性Ⅱ类错[牙合]患者垂直向颅面部骨关系的预测  

Prediction on vertical craniofacial bone relationship in patients with skeletal class Ⅱ malocclusion based on genetic algorithms method

作　　者：夏小雪[1] 杨陆一 滕蓉[1] 宁磊[1] 王守东[1] 牡琦丽 

机构地区：[1]吉林大学口腔医院正畸科,吉林长春130021

出　　处：《吉林大学学报（医学版）》2017年第4期794-799,共6页Journal of Jilin University：Medicine Edition

基　　金：吉林省科技厅科研基金资助课题(20150312021ZG)

摘　　要：目的:采用遗传算法建立不同垂直类型骨性Ⅱ类错牙合患者颅面部垂直向各标志点的定量关系方程,并将不同性别患者测量值用同一公式表达。方法:选取10~18岁未经治疗的骨性Ⅱ类错牙合患者共155例,根据下颌平面角(FH-MP和SN-MP)值分为高角组50例、均角组58例和低角组47例。每组随机选取5例作为检验样本,其余为实验样本。拍摄头颅侧位片并进行参数测量[前面高(N-Me)、上面高(N-Ans)、前鼻棘至上中切牙切缘垂直距离(Ans-U1)、下中切牙切缘至颏下点的垂直距离(L1-Me)、下面高(Ans-Me)、后面高(N-Go)、鼻根点至蝶鞍点的垂直距离(N-S)、蝶鞍点至关节点的垂直距离(S-Ar)、关节点至下颌角点的垂直距离(Ar-Go)、蝶鞍点至下颌角点的垂直距离(S-Go)、下颌角(∠Go)、面角(Facial angle)和后面高前面高比(S-Go/N-Me)]。确定与颅面结构相关的影响因子,采用遗传算法优化方程参数获得相关方程,将优化方程所得预测值和实测值进行误差比较。结果:高角、均角和低角组组内不同性别患者间各参数比较差异无统计学意义(P>0.05),将同一类型不同性别组合并进行组间比较,大部分参数差异有统计学意义(P<0.05)。相关分析显示,高角组患者,年龄与Ans-U1呈正相关关系(r=0.470),N-Me与Ans-Me呈正相关关系(r=0.964),Ans-U1与Ans-Me呈正相关关系(r=0.805)、与面角呈负相关关系(r=-0.023),N-Go与N-Me呈正相关关系(r=0.926),L1-Me与Ans-Me呈正相关关系(r=0.898)、与∠Go呈负相关关系(r=-0.468);均角组患者,年龄与N-Me呈正相关关系(r=0.531),Ans-U1与Ans-Me呈正相关关系(r=0.878)、与面角呈负相关关系(r=-0.262),Ans-Me与N-Me呈正相关关系(r=0.920)、与N-Ans呈负相关关系(r=-0.560)、与Ar-Go呈负相关关系(r=-0.652),N-Go与S-Go呈正相关关系(r=0.867)、与N-Ans呈正相关关系(r=0.252)、与L1-Me成正相关关系(r=0.754),S-Ar与S-Go呈正相关关系(r=0.671)、与Ar-Go呈负相关关系(Objective= To establish the quantitative relationship equation of the crantiofacial vertical points in the skeletal class Ⅱ malocclusion patients with various vertical types by using genetic algorithms method, and to express the measured values in the patients with different gender with the same formula. Methods： A total of 155 skeletal class Ⅱ malocclusion patients without treatment, aged from 10 to 18 years old, were selected and divided into high- angle group （n= 50）, average-angle group （n= 58）, low-angle group （n= 47） ; 5 samples were randomly selected in each group as the test samples, the rest as the experimental sample. The cephalometic radiographs were performed and measured. The relevant influencing factors of craniofacial structure were ensured. The genetic algorithm was used to optimize the equation parameters to obtain the correlation equation. The error between the predicted value and the measured value was compared. Results： The various parameters had no significant differences between different gender in high-angle, average-angle and low-angle groups （P^0.05）; then the men and the women with same type were combined, most of the indicators had statistically significant differences between three groups （P〈0.05）. The correlation analysis results showed that there was a positive correlation between age and Ans-U1 （r=0. 470）, there was a positive correlation between N-Me and Ans-Me （r=0. 964） ; for Ans-U1 , there was a positive correlation with Ans-Me （r=0. 805） and negative correlation with facial angle; there was a positive correlation between N-Go and N-Me （r=0. 926） ; for L1-Me, there was a positive correlation with Ans-Me （r= 0. 898） and negative correlation with the angle of Go （r=- 0. 468） in high-angle group. In average-angle group, there was a positive correlation between age and N-Me （r=0. 531） ; for Ans-U1, there was a positive correlation with Ans-Me （r= 0. 878） and negative correlation with the facial angle （r=--0. 262）; for

关 键 词：骨性Ⅱ类错[牙合] 垂直骨面型 生长预测 遗传算法 

分 类 号：R783.5[医药卫生—口腔医学]