Adolescents and Children Age Estimation Using Machine Learning Based on Pulp and Tooth Volumes on CBCT Images
10.12116/j.issn.1004-5619.2023.231210
- VernacularTitle: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
- Author:
Jia-Xuan HAN
1
;
Shi-Hui SHEN
;
Yi-Wen WU
;
Xiao-Dan SUN
;
Tian-Nan CHEN
;
Jiang TAO
Author Information
1. 上海交通大学医学院附属第九人民医院口腔综合科 上海交通大学口腔医学院 国家口腔医学中心 国家口腔疾病临床医学研究中心 上海市口腔医学重点实验室 上海市口腔医学研究所,上海 200011
- Keywords:
forensic anthropology;
forensic dentistry;
age estimation;
cone beam computed tomography(CBCT);
machine learning;
adolescents;
children
- From:
Journal of Forensic Medicine
2024;40(2):143-148
- CountryChina
- Language:Chinese
-
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.
