法医学杂志

法医学杂志 ›› 2024, Vol. 40 ›› Issue (2): 143-148.DOI: 10.12116/j.issn.1004-5619.2023.231210

• 法医人类学年龄推断专题 • 上一篇    

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

韩佳璇(), 沈诗慧, 吴怡文, 孙晓丹, 陈天南, 陶疆()   

  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
  • 收稿日期:2023-12-28 发布日期:2024-06-07 出版日期:2024-04-25
  • 通讯作者: 陶疆
  • 作者简介:韩佳璇（1999—），女，硕士研究生，主要从事牙齿及牙周组织发育的相关研究；E-mail：hanjx777@126.com
  • 基金资助:
    上海交通大学医工交叉研究基金资助项目(YG2019ZDA07)

Adolescents and Children Age Estimation Using Machine Learning Based on Pulp and Tooth Volumes on CBCT Images

Jia-xuan HAN(), Shi-hui SHEN, Yi-wen WU, Xiao-dan SUN, Tian-nan CHEN, Jiang TAO()   

  1. Department of General Dentistry, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University, School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai 200011, China
  • Received:2023-12-28 Online:2024-06-07 Published:2024-04-25
  • Contact: Jiang TAO

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摘要：

目的 利用锥形束计算机体层成像（cone beam computed tomography，CBCT）影像中左上颌中切牙与左上颌尖牙的牙髓体积和牙体体积，采用逐步回归法和机器学习方法分别推断青少年儿童年龄，并对推断效果进行比较分析。 方法 收集498例上海市汉族青少年儿童口腔颌面CBCT影像，测量左上颌中切牙与尖牙的牙髓体积和牙体体积并加以运算，运用K-最近邻、岭回归和决策树3种机器学习算法以及逐步回归法建立4个年龄推断模型，计算并比较决定系数、平均误差、均方根误差、均方误差和平均绝对误差等指标。绘制相关性热图，对参数间的单调关系进行可视化分析。 结果 K-最近邻模型（R2=0.779）和岭回归模型（R2=0.729）相对于逐步回归法（R2=0.617）表现更为优越，而决策树模型（R2=0.494）的拟合效果较差。相关性热图显示，年龄和牙髓体积、牙髓与牙体硬组织的体积比以及牙髓与牙体的体积比之间呈单调负相关。 结论 牙髓体积及牙髓体积占比与年龄之间存在密切关系，采用基于CBCT的机器学习方法能够提供更为准确的年龄推断结果，为进一步开展基于CBCT的深度学习牙龄推断研究奠定基础。

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

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 results. 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 absolute 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 demonstrated 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. Conclusion 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 foundation for further CBCT-based deep learning dental age estimation research.

Key words: forensic anthropology, forensic dentistry, age estimation, cone beam computed tomography （CBCT）, machine learning, adolescents, children

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