Li Ze, Xiao Ning, Nan Xiaoru, Chen Kejian, Zhao Yingjiao, Wang Shaobo, Guo Xiangjie, Gao Cairong
School of Forensic Medicine, Shanxi Medical University, Taiyuan, China.
Department of Orthodontics, Shanxi Provincial People's Hospital, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, China.
Front Dent Med. 2025 Jun 26;6:1618246. doi: 10.3389/fdmed.2025.1618246. eCollection 2025.
In forensic dentistry, dental age estimation assists experts in determining the age of victims or suspects, which is vital for legal responsibility and sentencing. The traditional Demirjian method assesses the development of seven mandibular teeth in pediatric dentistry, but it is time-consuming and relies heavily on subjective judgment.
This study constructed a largescale panoramic dental image dataset and applied various convolutional neural network (CNN) models for automated age estimation.
Model performance was evaluated using loss curves, residual histograms, and normal PP plots. Age prediction models were built separately for the total, female, and male samples. The best models yielded mean absolute errors of 1.24, 1.28, and 1.15 years, respectively.
These findings confirm the effectiveness of deep learning models in dental age estimation, particularly among northern Chinese adolescents.
在法医牙科学中，牙齿年龄估计有助于专家确定受害者或嫌疑人的年龄，这对于法律责任判定和量刑至关重要。传统的德米尔坚方法评估儿科牙科中七颗下颌牙齿的发育情况，但该方法耗时且严重依赖主观判断。
本研究构建了一个大规模全景牙科图像数据集，并应用各种卷积神经网络（CNN）模型进行自动年龄估计。
使用损失曲线、残差直方图和正态概率图评估模型性能。分别为总样本、女性样本和男性样本建立了年龄预测模型。最佳模型的平均绝对误差分别为1.24年、1.28年和1.15年。
这些发现证实了深度学习模型在牙齿年龄估计中的有效性，尤其是在中国北方青少年中。
