OBJECTIVE: To investigate the age-related changes of the mandibular third molar root pulp visibility in individuals in East China, and to explore the feasibility of applying this method to determine whether an individual is 18 years or older.
METHODS: A total of 1 280 oral panoramic images were collected from the 15-30 years old East China population, and the mandibular third molar root pulp visibility in all oral panoramic images was evaluated using OLZE 0-3 four-stage method, and the age distribution of the samples at each stage was analyzed using descriptive statistics.
RESULTS: Stages 0, 1, 2 and 3 first appeared in 16.88, 19.18, 21.91 and 25.44 years for males and in 17.47, 20.91, 22.01 and 26.01 years for females. In all samples, individuals at stages 1 to 3 were over 18 years old.
CONCLUSIONS: It is feasible to determine whether an individual in East China is 18 years or older based on the mandibular third molar root pulp visibility on oral panoramic images.
目的: 研究华东地区个体下颌第三磨牙根管可见度的增龄性变化，探讨应用其判断个体是否年满18周岁的可行性。方法: 共收集1 280例华东地区15~30周岁人群的口腔全景片，应用OLZE等提出的方法（0~3 4个阶段）评估所有口腔全景片中下颌第三磨牙根管可见度，对各阶段的样本年龄分布进行描述性统计分析。结果: 男性首次出现阶段0、1、2、3的年龄分别为16.88、19.18、21.91、25.44岁，女性分别为17.47、20.91、22.01、26.01岁。所有样本中，阶段1~3的个体年龄均超过18周岁。结论: 基于口腔全景片中下颌第三磨牙根管可见度判断华东地区个体是否年满18周岁的方法具有一定的可行性。.

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.
CONCLUSIONS: 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.
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。.

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 10 256 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 1.246 3, 1.281 8 and 1.153 8 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.
CONCLUSIONS: 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.
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10 256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1.246 3、1.281 8和1.153 8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。.

In the study of age estimation in living individuals, a lot of data needs to be analyzed by mathematical statistics, and reasonable medical statistical methods play an important role in data design and analysis. The selection of accurate and appropriate statistical methods is one of the key factors affecting the quality of research results. This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics, difference analysis, consistency test and multivariate statistical analysis, as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals, and summarizes the relevance and application prospects between medical statistical methods and machine learning methods. This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results.
活体年龄推断研究中通常需要对大量的数据进行数理统计分析，合理的医学统计方法在数据整理和分析中发挥着重要作用，选择准确、恰当的统计方法是影响研究结果质量的关键因素之一。本文综述了活体年龄推断研究中描述性统计、差异性分析、一致性检验、多元统计分析等较为常用的医学统计方法以及浅层学习、深度学习等机器学习方法的原理和适用原则，并概括介绍了医学统计方法和机器学习方法之间的关联性和应用前景，旨在为活体年龄推断研究获得更为科学、精准的结果提供技术指引。.

Dental age estimation is a crucial aspect and one of the ways to accomplish forensic age estimation, and imaging technology is an important technique for dental age estimation. In recent years, some studies have preliminarily confirmed the feasibility of magnetic resonance imaging (MRI) in evaluating dental development, providing a new perspective and possibility for the evaluation of dental development, suggesting that MRI is expected to be a safer and more accurate tool for dental age estimation. However, further research is essential to verify its accuracy and feasibility. This article reviews the current state, challenges and limitations of MRI in dental development and age estimation, offering reference for the research of dental age assessment based on MRI technology.
牙龄推断是法医学年龄推断的重要内容和实现路径之一，影像技术是牙龄推断的重要技术手段。近年来，有研究初步证实了MRI在评估牙发育方面的可行性，该技术为牙发育评估提供了新的视角和可能性，有望成为更安全且准确的牙龄推断技术手段，但仍需进一步验证其准确性和适应性。本文综述了MRI技术在牙发育和年龄推断中的研究现状、研究瓶颈和局限性，为基于MRI技术的牙龄推断研究提供参考和借鉴。.

Taurodontism is a dental morphological anomaly characterized by enlarged pulp cavities repositioned towards the apical region of the tooth, coupled with shortened root structures. Molars are commonly affected by this alteration. Certain populations exhibit up to 48% prevalences for this dental alteration, underscoring its significance in dental age estimation (DAE). In the field of DAE, an individual\'s chronological age is inferred from specific dental features, frequently employed within the forensic context. The effect of taurodontism on the features of DAE is an unanswered issue. The influence of taurodontism on eruption, mineralization, radiographic visibility of root canals, and radiographic visibility of the periodontal ligament space in mandibular third molars- some of the established criteria for DAE as examples-is currently not systematically examined. Some common staging scales for the dental features of DAE cannot technically be applied to taurodontic teeth. Additionally, given the association of taurodontism with syndromes affecting tooth development, caution is warranted in age assessment procedures. Notably, taurodontic teeth may serve as indicators of syndromes influencing skeletal development, further emphasizing the relevance of taurodontism in forensic age assessment. Presumably taurodontic teeth were included in reference data to some extent due to their partially high prevalence in the past, whereby the influence of taurodontism has been statistically absorbed within the overall spread of the features. Future studies should compare the temporal course of these tooth characteristics in affected and unaffected teeth. Subsequent initiatives should focus on raising awareness among forensic dentists regarding taurodontism, necessitating in-depth exploration of the subject.

OBJECTIVE: Age estimation plays a critical role in personal identification, especially when determining compliance with the age of consent for adolescents. The age of consent refers to the minimum age at which an individual is legally considered capable of providing informed consent for sexual activities. The purpose of this study is to determine whether adolescents meet the age of 14 or 18 by using dental development combined with machine learning.
METHODS: This study combines dental assessment and machine learning techniques to predict whether adolescents have reached the consent age of 14 or 18. Factors such as the staging of the third molar, the third molar index, and the visibility of the periodontal ligament of the second molar are evaluated.
RESULTS: Differences in performance metrics indicate that the posterior probabilities achieved by machine learning exceed 93% for the age of 14 and slightly lower for the age of 18.
CONCLUSIONS: This study provides valuable insights for forensic identification for adolescents in personal identification, emphasizing the potential to improve the accuracy of age determination within this population by combining traditional methods with machine learning. It underscores the importance of protecting and respecting the dignity of all individuals involved.

OBJECTIVE: To evaluate the applicability and accuracy of Demirjian and Chaillet method in estimating the actual age of Uygur and Han children and adolescents in Urumqi.
METHODS: A total of 1144 orthopantomograms were included in the study, and the seven permanent teeth in the left jaw were divided into different stages according to two dental age estimation methods, and the dental age was converted to tooth age after checking the table and assigning points, and the dental age and its chronological age were compared with t test or rank sum test using SPSS 21.0 software package, and the accuracy of the two methods was evaluated by comparing the mean absolute error of the two methods.
RESULTS: Demirjian method was overestimated by an average of 0.46 years (0.47 years for males and 0.43 years for women) in the Han population, 0.36 years for men and 0.26 years for women in Uyghur population, the difference was significant between Uyghur and Han boys (P＜0.05). Chaillet method yielded an average underestimate of 0.01 years (0.04 years for men and -0.08 years for women) in the Han population, and 0.08 years(0.02 years for men and -0.21 years for women) in the Uyghur population, there was no significant difference between Uyghur and Han boys and girls(P＞0.05).
CONCLUSIONS: When assessing the age of Uyghurhan children and adolescents in Urumqi, Chaillet method is more accurate than the Demirjian method. When applying dental age estimation method in different regions, it is necessary to evaluate the accuracy of the estimation method and revise it if necessary to improve the accuracy.

Adult age estimation is one of the most challenging problems in forensic science and physical anthropology. In this study, we aimed to develop and evaluate machine learning (ML) methods based on the modified Gustafson\'s criteria for dental age estimation. In this retrospective study, a total of 851 orthopantomograms were collected from patients aged 15 to 40 years old. The secondary dentin formation (SE), periodontal recession (PE), and attrition (AT) of four mandibular premolars were analyzed according to the modified Gustafson\'s criteria. Ten ML models were generated and compared for age estimation. The partial least squares regressor outperformed other models in males with a mean absolute error (MAE) of 4.151 years. The support vector regressor (MAE = 3.806 years) showed good performance in females. The accuracy of ML models is better than the single-tooth model provided in the previous studies (MAE = 4.747 years in males and MAE = 4.957 years in females). The Shapley additive explanations method was used to reveal the importance of the 12 features in ML models and found that AT and PE are the most influential in age estimation. The findings suggest that the modified Gustafson method can be effectively employed for adult age estimation in the southwest Chinese population. Furthermore, this study highlights the potential of machine learning models to assist experts in achieving accurate and interpretable age estimation.

The age determination of individuals, especially minors, is critical in forensic research. In forensic practice, dental age estimation is one of the most commonly used methods for determining age as teeth are easy to preserve and relatively resistant to environmental factors. Tooth development is affected and regulated by genetic factors; however, these are not incorporated into current commonly used tooth age inference methods, leading to unreliable results. Here, we established a Demirjian and a Cameriere tooth age estimation-based methods suitable for use in children in southern China. By using the difference between the inferred age and the actual age (MD) as the phenotype, we identified 65 and 49 SNPs related to tooth age estimation from 743,722 loci among 171 children in southern China through a genome-wide association analysis (p<0.0001). We also conducted a genome-wide association study on dental development stage (DD) using the Demirjian tooth age estimation method and screened two sets of SNP sites (52 and 26) based on whether age difference was considered. The gene function enrichment analysis of these SNPs found that they were related to bone development and mineralization. Although SNP sites screened based on MD seem to improve the accuracy of tooth age estimation, there is little correlation between these SNPs and an individual\'s Demirjian morphological stage. In conclusion, we found that individual genotypes can affect tooth age estimation, and based on different phenotypic analysis models, we have identified some novel SNP sites related to tooth age inference and Demirjian\'s tooth development stage. These studies provide a reference for subsequent phenotypic selection based on tooth age inference analysis, and the results could possibly be used in the future to make forensic age estimation more accurate.