口腔医学研究 ›› 2016, Vol. 32 ›› Issue (11): 1156-1159.DOI: 10.13701/j.cnki.kqyxyj.2016.11.011

• 临床研究论著 • 上一篇    下一篇

基于多元线性回归模型的牙齿比色方法

于皓1,2,张栋3,程少龙1,2,程辉1,2*   

  1. 1. 福建医科大学附属口腔医院修复科 福建 福州 350002;
    2. 福建省高校口腔医学重点实验室;
    3. 福州大学数学与计算机科学学院
  • 收稿日期:2016-04-26 出版日期:2016-11-25 发布日期:2016-11-25
  • 通讯作者: 程辉,E-mail:ch.fj@163.com
  • 作者简介:于皓(1982~ ),男,福建人,博士,副主任医师,主要从事口腔修复学临床及基础的研究工作。
  • 基金资助:
    国家自然科学基金(编号:81200824)
    教育部留学归国人员科研基金(编号:2013B020)
    福建省青年拔尖人才专项经费(编号:2014)
    福建省医学创新课题(编号:2015-CX-31)

Prediction the Color of Tooth: A Method Based on Linear Regression

YU Hao1,2, ZHANG Dong3, CHENG Shao-long1,2, CHENG Hui1,2*   

  1. 1. Departmentof Prosthodontics, School and Hospital of Stomatology, Fujian Medical University, Fuzhou 350002, China;
    2. Key Laboratory of Stomatology (FJMU), Fujian Province University, Fuzhou 350002, China;
    3. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350002, China
  • Received:2016-04-26 Online:2016-11-25 Published:2016-11-25

摘要: 目的:运用机器学习领域相关算法和计算机选色技术,建立一种基于线性回归模型的牙齿比色方法。方法:招募123名来自福建医科大学口腔医学院的本科生作为志愿者,平均年龄(22.48±1.77)岁,男64人,女59人。使用分光光度计测量志愿者上颌牙(15-25)的色度值(CIE L*、a*、b*值),测量部位为牙冠唇侧中1/3,每颗牙测量5次取平均值。根据所测数据首先确定色度值的特征值的矩阵X和线性回归系数的矩阵θ,接着构建评价函数并用协同过滤算法对其进行优化,最后建立预测缺失牙色度值的多元线性回归模型。随机选取24名同龄志愿者,利用该回归模型分别计算得到24名志愿者240颗牙每颗牙的L*、a*和b*值的预测值,并分析其与分光光度计所测实际值的接近程度。结果:94.17%的志愿者牙齿L*值的预测准确度超过80%,100%的志愿者牙齿a*值和b*值的预测准确度都超过了80%。L*值预测准确率的95%置信区间(79.00%~100%),a*值预测准确率的95%置信区间(94.44%~100%),b*值预测准确率的95%置信区间(86.67%~100%)。结论:本文所用线型回归模型对于牙齿色度值的预测是可行并且有参考价值的。

关键词: 比色, 预测, 色度值, 线性回归模型

Abstract: Objective: To establish a linear regression model for shade matching based on machine learning algorithms. Methods: One hundred and twenty-three undergraduates were recruited as the volunteers of the present study(mean age: 22.48±1.77 years old, male: female=64:59). The chromatic valuesof volunteer’s maxillary teeth (15-25) were measured with a spectrophotometer. The matrix of chromatic value (X) and regression coefficient (θ) were determined based on the data from the volunteers. Efforts have then been put into the optimizing evaluation function through collaborative filtering. Finally, a multiple linear regression model was established with the purpose to predict the chromatic value of missing tooth. Twenty-four volunteers at similar ages were sampled to evaluate the prediction accuracy of the multiple linear regression model. Thepredicted chromatic value of their teeth were calculated and compared to the measured values based on the spectrophotometer. Results: The prediction accuracy of the model on L*value was more than 80% among 94.17% of 240 teeth, while the prediction accuracy ona*value and b*value were more than 80% among the entire sampling teeth. Conclusion: The multiple linear regression model might be a suitable tool to predict the color of tooth in permanent dentition.

Key words: Shade matching, Prediction, Chromatic value, Linear regression model

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