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

发布时间：2018-03-27 20:24

本文选题：骨性Ⅱ类　切入点：垂直骨面型　出处：《吉林大学》2017年硕士论文

【摘要】：目的:使用遗传算法(Genetic Algorithms,简称GAS)建立不同垂直类型骨性Ⅱ类患者颅面部垂直向各标志点的方程,并将不同性别用同一公式表达。方法:选取10-18岁未经治疗的骨性Ⅱ类患者共155名,高角组(A组)50人:13.7±2.4岁;均角组(B组)58人:13.2±2.5岁;低角组(C组)47人:13.4±2.8岁。每组中随机选取5人为检验样本,其余为实验样本。拍摄头颅侧位片,并进行测量、分析(Coben、SteinerTweed北医大)。测量结果进行独立样本t检验、单因素方差分析及逐步回归分析,找到颅面结构的相关影响因子;使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:1 SPSS分析显示同一类型不同性别颅面部垂直向的相关项目均无统计学差异(P0.05)。2对A、B、C三组进行单因素方差分析;组间两两比较采用SNK-q检验,比较三组间颅面部骨间存在相关性(P0.05)。3对A、B、C三组颅面部骨各项目进行逐步回归分析,识别相关影响因子。4使用遗传算法建立了不同垂直类型骨性Ⅱ类患者颅面部骨垂直标志点的关系方程。5 GAS建立方程的预测值与实测数据进行比较,GAS法误差小,精度高,但无统计学意义(P0.05)。结论:1.同一类型不同性别颅面部垂直向骨无差异。2.不同垂直类型骨性Ⅱ类患者垂直向颅面部骨间存在差异。3.同种垂直类型骨性Ⅱ类患者垂直向颅面部骨间关系密切。4.GAS法建立的预测方程精度高,且直观、清晰的表明不同垂直类型骨性Ⅱ类患者颅面部骨的关系,并可进行一定程度上的生长预测。
[Abstract]:Objective: using genetic algorithm (Genetic Algorithms, referred to as GAS) to establish different types of vertical skeletal Class II patients with craniofacial vertical landmarks of the equation, and the different sex with the same expression. Methods: a total of 10-18 years of age without skeletal Class II patients with a total of 155, the high angle group (A group) 50: 13.7 + 2.4 years; the average angle group (B group) of 58 people: 13.2 + 2.5 years; the low angle group (C group) of 47 people: 13.4 + 2.8 years. Randomly selected in each group 5 as test samples, the experimental samples. Cephalometric film, and measurement, analysis (Coben, SteinerTweed PUHSC). The measurement results of independent sample t test, single factor variance analysis and stepwise regression analysis, find the influence of craniofacial structure factor; using genetic algorithm to optimize the parameters to get the equation of correlation equation; the optimization equation of the prediction values and the measured values are compared in error. Results: 1 SPSS analysis showed the same There was no significant difference in different sex types of craniofacial vertical related project (P0.05).2 on A, B, C three groups of single factor variance analysis; comparison between the 22 groups using SNK-q test, comparison between the three groups of craniofacial bone correlation between (P0.05).3 on A, B, stepwise regression analysis C three groups of craniofacial bones of the project, influencing factor.4 recognition and prediction using genetic algorithm is established based on.5 GAS equations with different vertical types of skeletal Class II patients with craniofacial bone vertical landmarks the equation's values are compared with the measured data, GAS method, small error, high precision, but without statistical significance (P0.05). Conclusion: 1. different sex craniofacial vertical bone.2. had no significant differences in different vertical types of skeletal Class II patients with vertical craniofacial bone differences between.3. with vertical type of skeletal Class II patients with craniofacial bone to the vertical relationship between closely established.4.GAS method The accuracy of the prediction equation is high, and the relationship between the craniofacial bones of different vertical types of skeletal Class II patients is clearly demonstrated and the growth prediction can be carried out to a certain extent.

【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R783.5

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