Fan Fei, Ke Wenchi, Dai Xinhua, Shi Lei, Liu Yuanyuan, Lin Yushan, Cheng Ziqi, Zhang Yi, Chen Hu, Deng Zhenhua
West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, People's Republic of China.
College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, 610065, People's Republic of China.
Int J Legal Med. 2023 May;137(3):721-731. doi: 10.1007/s00414-023-02956-9. Epub 2023 Jan 31.
Teeth-based age and sex estimation is an important task in mass disasters, criminal scenes, and archeology. Although various methods have been proposed, most of them are subjective and influenced by observers' experiences. In this study, we aimed to develop a deep learning model for automatic dental age and sex estimation from orthopantomograms (OPGs) and compare to manual methods. A large dataset of 15,195 OPGs (age range, 16 ~ 50 years; mean age, 29.65 years ± 9.36 [SD]; 10,218 females) was used to train and test a hybrid deep learning model which is a combination of convolutional neural network and transformer model. The final performance of this model was evaluated on additional independent 100 OPGs and compared to the manual method for external validation. In the test of 1413 OPGs, the mean absolute error (MAE) of age estimation was 2.61 years by this model. The accuracy and the area under the receiver operating characteristic curve (AUC) of sex estimation were 95.54% and 0.984. The heatmap indicated that the crown and pulp chamber of premolars and molars contain the most age-related information. In the additional independent 100 OPGs, this model achieved an MAE of 3.28 years for males and 3.79 years for females. The accuracy of this model was much higher than that of the manual models. Therefore, this model has the potential to assist radiologists in automated age and sex estimation.
基于牙齿的年龄和性别估计是大规模灾难、犯罪现场和考古学中的一项重要任务。尽管已经提出了各种方法，但大多数方法都具有主观性，并且受观察者经验的影响。在本研究中，我们旨在开发一种深度学习模型，用于从口腔全景片（OPG）中自动估计牙齿年龄和性别，并与手动方法进行比较。使用一个包含15195张OPG的大型数据集（年龄范围为16至50岁；平均年龄为29.65岁±9.36[标准差]；女性10218名）来训练和测试一种混合深度学习模型，该模型是卷积神经网络和Transformer模型的组合。该模型的最终性能在另外100张独立的OPG上进行评估，并与手动方法进行比较以进行外部验证。在对1413张OPG的测试中，该模型年龄估计的平均绝对误差（MAE）为2.61岁。性别估计的准确率和受试者工作特征曲线下面积（AUC）分别为95.54%和0.984。热图表明，前磨牙和磨牙的牙冠和牙髓腔包含与年龄相关的最多信息。在另外100张独立的OPG中，该模型对男性的MAE为3.28岁，对女性为3.79岁。该模型的准确率远高于手动模型。因此，该模型有潜力协助放射科医生进行自动年龄和性别估计。
