法医学杂志2024，Vol.40Issue(2)：143-148,6.DOI:10.12116/j.issn.1004-5619.2023.231210
基于CBCT中牙髓和牙体体积的机器学习用于青少年儿童年龄推断
Adolescents and Children Age Estimation Using Machine Learning Based on Pulp and Tooth Volumes on CBCT Images
摘要
Abstract
Objective To estimate adolescents and children age using stepwise regression and machine learning methods based on the pulp and tooth volumes of the left maxillary central incisor and cuspid on cone beam computed tomography(CBCT)images,and to compare and analyze the estimation re-sults.Methods A total of 498 Shanghai Han adolescents and children CBCT images of the oral and maxillofacial regions were collected.The pulp and tooth volumes of the left maxillary central incisor and cuspid were measured and calculated.Three machine learning algorithms(K-nearest neighbor,ridge regression,and decision tree)and stepwise regression were used to establish four age estimation models.The coefficient of determination,mean error,root mean square error,mean square error and mean ab-solute error were computed and compared.A correlation heatmap was drawn to visualize and the monotonic relationship between parameters was visually analyzed.Results The K-nearest neighbor model(R2=0.779)and the ridge regression model(R2=0.729)outperformed stepwise regression(R2=0.617),while the decision tree model(R2=0.494)showed poor fitting.The correlation heatmap demon-strated a monotonically negative correlation between age and the parameters including pulp volume,the ratio of pulp volume to hard tissue volume,and the ratio of pulp volume to tooth volume.Con-clusion Pulp volume and pulp volume proportion are closely related to age.The application of CBCT-based machine learning methods can provide more accurate age estimation results,which lays a founda-tion for further CBCT-based deep learning dental age estimation research.关键词
法医人类学/法医齿科学/年龄推断/锥形束计算机体层成像/机器学习/青少年/儿童Key words
forensic anthropology/forensic dentistry/age estimation/cone beam computed tomography(CBCT)/machine learning/adolescents/children分类
医药卫生引用本文复制引用
韩佳璇,沈诗慧,吴怡文,孙晓丹,陈天南,陶疆..基于CBCT中牙髓和牙体体积的机器学习用于青少年儿童年龄推断[J].法医学杂志,2024,40(2):143-148,6.基金项目
上海交通大学医工交叉研究基金资助项目(YG2019ZDA07) （YG2019ZDA07）
