摘要
目的探讨影响青少年下颌磨牙后区生长潜力的因素,并分析基于遗传算法(genetic algorithms,GAS)优化青少年下颌磨牙后区生长潜力非线性预测方程的准确性。方法选取2017—2019年于吉林大学口腔医院正畸科就诊的符合纳入标准的初诊患者306例。通过头颅定位侧位片测量下颌磨牙后间隙及颈椎测量指标,采用多元线性回归分析下颌磨牙后间隙与年龄、下颌第三磨牙牙龄、颈椎骨龄等的相关性,选取最强相关因子构建线性回归方程和基于GAS优化的非线性方程,并比较两个预测方程的准确性。结果(1)不同矢状骨面型患者下颌磨牙后间隙不同,骨性Ⅰ类错畸形组[(9.99±2.53)mm]与骨性Ⅲ类错畸形组[(10.53±3.53)mm]比较,差异无统计学意义(P>0.05);而骨性Ⅱ类错畸形组患者下颌磨牙后间隙[(8.98±2.71)mm]小于骨性Ⅰ类和Ⅲ类错畸形组,差异均有统计学意义(均P<0.05)。故将所有患者分为骨性Ⅰ+Ⅲ类错畸形组和骨性Ⅱ类错畸形组。(2)下颌磨牙后间隙(RMS)与下颌第三磨牙牙龄(YL)呈正相关关系,两组分别构建线性回归方程和基于GAS优化的非线性方程如下。骨性Ⅰ+Ⅲ类错畸形组:RMS=1.489YL+3.891;RMS=2.36YL0.81+2.686。骨性Ⅱ类错畸形组:RMS=1.464YL+1.961;RMS=2.36YL0.81+0.723。基于GAS优化的非线性方程误差值低于线性回归方程,但其差异无统计学意义(P>0.05)。结论临床正畸医生进行青少年患者正畸方案设计时,应着重结合患者第三磨牙牙龄及矢状骨面型。应用基于GAS优化的非线性方程预测下颌磨牙后区生长潜力具有较高的预测精度和准确性。
Objective To explore the influencing factors of the growth potential of mandibular retromolar region in adolescents,and analyze the accuracy of non-linear equation based on genetic algorithms(GAS)optimization.Methods A total of 306 patients were selected according to the inclusion criteria,who were treated at the Department of Orthodontics in Jilin University Stomatological Hospital from 2017 to 2019.The lateral cephalometric radiographs were used to obtain the mandibular retromolar space and cervical vertebrae measurements.Multiple linear regression analysis was used to evaluate the correlation between retromolar space and age,dental age of the third mandibular molar,cervical vertebral maturation,etc.The strongest correlation factor was selected to construct a linear regression equation and a non-linear equation based on GAS optimization,and the accuracy of the two prediction equations were compared.Results(1)The retromolar space was different in different antero-posterior skeletal facial types.There was no statistically significant difference between the skeletal classⅠmalocclusion group[(9.99±2.53)mm]and the skeletal classⅢmalocclusion group[(10.53±3.53)mm](P>0.05).In the skeletal classⅡmalocclusion group[(8.98±2.71)mm]it was smaller than the skeletal classⅠand classⅢmalocclusion group,the difference being statistically significant(all P<0.05).Therefore,all patients were divided into skeletal classⅠ+Ⅲmalocclusion group and skeletal classⅡmalocclusion group.(2)The retromolar space(RMS)was positively correlated with the third molar age(YL).The linear regression equations and nonlinear equations based on GAS optimization were constructed respectively:for skeletal classⅠ+Ⅲgroup:RMS=1.489 YL+3.891,and RMS=2.36 YL0.81+2.686;for skeletal classⅡgroup:RMS=1.464 YL+1.961,and RMS=2.36 YL0.81+0.723.The error value of the nonlinear equation optimized based on GAS was lower than the linear regression equation,but the difference was not statistically significant(P>0.05).Conclusion Clinical orthodonti
作者
闫婧
赵雪娇
于淼
王立摇
李真真
徐依山
杨陆一
YAN Jing;ZHAO Xue-jiao;YU Miao;WANG Li-yao;LI Zhen-zhen;XU Yi-shan;YANG Lu-yi(Department of Orthodontics,Stomatological Hospital,Jilin University,Changchun 130021,China)
出处
《中国实用口腔科杂志》
CAS
2020年第9期546-550,554,共6页
Chinese Journal of Practical Stomatology
基金
吉林省卫生技术创新项目(2017J069)。
关键词
磨牙后间隙
遗传算法
牙龄
颈椎骨龄
矢状骨面型
retromolar space
genetic algorithm
dental age
cervical vertebral maturation
antero-posterior skeletal facial types
