Demirjian法结合机器学习算法推断北方汉族儿童及青少年牙龄  

Dental Age Estimation in Northern Chinese Han Children and Adolescents Using Demirjian’s Method Combined with Machine Learning Algorithms

作　　者：郭瑜鑫 卜雯卿 唐羽 吴迪 杨徽 孟昊天[1] 郭昱成[1,2] GUO Yu-xin;BU Wen-qing;TANG Yu;WU Di;YANG Hui;MENG Hao-tian;GUO Yu-cheng(Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research,Hospital of Stomatology,Xi’an Jiaotong University,Xi’an 710004,China;Department of Orthodontics,Hospital of Stomatology,Xi’an Jiaotong University,Xi’an 710004,China)

机构地区：[1]陕西省颅颌面精准医学研究重点实验室,西安交通大学口腔医院,陕西西安710004 [2]西安交通大学口腔医院正畸科,陕西西安710004

出　　处：《法医学杂志》2024年第2期135-142,共8页Journal of Forensic Medicine

基　　金：国家自然科学基金青年基金资助项目(81701869)。

摘　　要：目的探讨Demirjian法结合机器学习算法在北方汉族儿童及青少年牙龄推断中的应用价值。方法收集10256例我国北方汉族5~24岁人群的口腔全景片,运用Demirjian法对左下颌8颗恒牙的发育进行分期,并结合支持向量回归、梯度提升回归、线性回归、随机森林回归和决策树回归等多种机器学习算法,分别基于总样本、女性样本和男性样本构建年龄推断模型,并评价不同机器学习算法在3组样本中的拟合性能。结果对于总样本和女性样本,推断准确率最高的模型均为支持向量回归模型;对于男性样本,推断准确率最高的模型为梯度提升回归模型。最佳年龄推断模型在总样本、女性样本和男性样本的平均绝对误差分别为1.2463、1.2818和1.1538岁。最佳年龄推断模型对各年龄区间的推断准确率不同,对于18岁以下人群的年龄推断相对准确。结论本研究构建的年龄推断机器学习模型在我国北方汉族儿童及青少年中具有较好的准确率,但在成年人群中的推断效果不理想,可以考虑联合其他变量以提高年龄推断的准确性。Objective To investigate the application value of combining the Demirjian’s method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents.Methods Oral panoramic images of 10256 Han individuals aged 5 to 24 years in northern China were collected.The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian’s method.Various machine learning algorithms,including support vector regression(SVR),gradient boosting regression(GBR),linear regression(LR),random forest regression(RFR),and decision tree regression(DTR)were employed.Age estimation models were constructed based on total,female,and male samples respectively using these algorithms.The fitting performance of different machine learning algorithms in these three groups was evaluated.Results SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples,while GBR showed the best performance in male samples.The mean absolute error(MAE)of the optimal age estimation model was 1.2463,1.2818 and 1.1538 years in the total,female and male samples,respectively.The optimal age estimation model exhibited varying levels of accuracy across different age ranges,which provided relatively accurate age estimations in individuals under 18 years old.Conclusion The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents.However,its performance is not ideal when applied to adult population.To improve the accuracy in age estimation,the other variables can be considered.

关 键 词：法医人类学 法医齿科学 年龄推断 机器学习 DEMIRJIAN法 口腔全景片 儿童 青少年 

分 类 号：R89[医药卫生—法医学] DF795.6[医药卫生—临床医学] D919.6[政治法律—诉讼法学]