Abstract：
The aim of this study is to compare a technique using Convolutional Neural Network (CNN) with the Demirjian\'s method for chronological age estimation of living individuals based on tooth age from panoramic radiographs. This research used 5898 panoramic X-ray images collected for diagnostic from pediatric patients aged 4-17 who sought treatment at Antalya Oral and Dental Health Hospital between 2015 and 2020. The Demirjian\'s method\'s grading was executed by researchers who possessed appropriate training and experience. In the CNN method, various CNN architectures including Alexnet, VGG16, ResNet152, DenseNet201, InceptionV3, Xception, NASNetLarge, InceptionResNetV2, and MobieNetV2 have been evaluated. Densenet201 exhibited the lowest MAE value of 0.73 years, emphasizing its superior accuracy in age estimation compared to other architectures. In most age categories, the predicted age closely matches the actual age. The most inconsistent results are observed at ages 12 and 13. The results highlight correspondence between the age predicted by CNN and the Demirjian\'s approach. In conclusion, the results show that the CNN method is adequate to be an alternative to the Demirjian\'s age estimation method. We suggest that convolutional neural network can effectively optimize the accuracy of age estimation and can be faster than traditional methods, eliminating the need for additional learning from experts.
摘要：
这项研究的目的是将使用卷积神经网络（CNN）的技术与Demirjian的方法进行比较，以根据全景射线照片中的牙齿年龄对活着的个体进行实际年龄估计。这项研究使用了从2015年至2020年在安塔利亚口腔和牙科健康医院寻求治疗的4-17岁儿科患者中收集的5898张全景X射线图像进行诊断。Demirjian方法的评分是由具有适当培训和经验的研究人员执行的。在CNN方法中，各种CNN架构，包括Alexnet，VGG16,ResNet152,DenseNet201,InceptionV3,Xception,NASNetLarge,已对InceptionResNetV2和MobieNetV2进行了评估。Densenet201表现出0.73年的最低MAE值，强调其在年龄估计方面优于其他架构。在大多数年龄类别中，预测年龄与实际年龄非常吻合。在12岁和13岁时观察到最不一致的结果。结果突出了CNN预测的年龄与Demirjian方法之间的对应关系。总之,结果表明，CNN方法足以替代Demirjian的年龄估计方法。我们建议卷积神经网络可以有效地优化年龄估计的准确性，并且可以比传统方法更快，消除了向专家学习的需要。
