Automatic dental age estimation in adolescents via oral panoramic imaging.

IF 1.8 Q3 DENTISTRY, ORAL SURGERY & MEDICINE

Frontiers in dental medicine Pub Date : 2025-06-26 eCollection Date: 2025-01-01 DOI:10.3389/fdmed.2025.1618246

Ze Li, Ning Xiao, Xiaoru Nan, Kejian Chen, Yingjiao Zhao, Shaobo Wang, Xiangjie Guo, Cairong Gao

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Abstract

Object: 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.

Methods: This study constructed a largescale panoramic dental image dataset and applied various convolutional neural network (CNN) models for automated age estimation.

Results: 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.

Discussion: These findings confirm the effectiveness of deep learning models in dental age estimation, particularly among northern Chinese adolescents.

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利用口腔全景成像技术自动估计青少年牙齿年龄。

目的：在法医牙医学中，牙龄估算有助于专家确定被害人或犯罪嫌疑人的年龄，对法律责任和量刑具有重要意义。传统的Demirjian法在儿童牙科中对7颗下颌骨牙齿的发育进行评估，但费时且严重依赖主观判断。方法：构建大规模牙齿全景图像数据集，应用各种卷积神经网络（CNN）模型进行自动年龄估计。结果：使用损失曲线、残差直方图和正态PP图评估模型性能。分别为总样本、女性样本和男性样本建立年龄预测模型。最佳模型的平均绝对误差分别为1.24、1.28和1.15年。讨论：这些发现证实了深度学习模型在牙齿年龄估计方面的有效性，特别是在中国北方青少年中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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