PDF(Pubmed)
Abstract：
Accurate age estimation is vital for clinical and forensic purposes. With the rapid advancement of artificial intelligence(AI) technologies, traditional methods relying on tooth development, while reliable, can be enhanced by leveraging deep learning, particularly neural networks. This study evaluated the efficiency of an AI model by applying the entire panoramic image for age estimation. The outcome performances were analyzed through supervised learning (SL) models.
Total of 27,877 dental panorama images from 5 to 90 years of age were classified by 2 types of grouping. In type 1 they were classified by each age and in type 2, applying heuristic grouping, the age over 20 years were classified by every 5 years. Wide ResNet (WRN) and DenseNet (DN) were used for supervised learning. In addition, the analysis with ± 3 years of deviation in both types were performed.
For the DN model, while the type 1 grouping achieved an accuracy of 0.1016 and F1 score of 0.058, the type 2 achieved an accuracy of 0.3146 and F1 score of 0.2027. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.281, 0.7323 respectively; and the F1 score were 0.1768, 0.6583 respectively. For the WRN model, while the type 1 grouping achieved an accuracy of 0.1041 and F1 score of 0.0599, the type 2 achieved an accuracy of 0.3182 and F1 score of 0.2071. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.2716, 0.7323 respectively; and the F1 score were 0.1709, 0.6437 respectively.
The application of entire panorama image data for supervised with classification by heuristics grouping with ± 3years of deviation for supervised learning models and demonstrated satisfactory outcome for the age estimation.
Total of 27,877 dental panorama images from 5 to 90 years of age were classified by 2 types of grouping. In type 1 they were classified by each age and in type 2, applying heuristic grouping, the age over 20 years were classified by every 5 years. Wide ResNet (WRN) and DenseNet (DN) were used for supervised learning. In addition, the analysis with ± 3 years of deviation in both types were performed.
For the DN model, while the type 1 grouping achieved an accuracy of 0.1016 and F1 score of 0.058, the type 2 achieved an accuracy of 0.3146 and F1 score of 0.2027. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.281, 0.7323 respectively; and the F1 score were 0.1768, 0.6583 respectively. For the WRN model, while the type 1 grouping achieved an accuracy of 0.1041 and F1 score of 0.0599, the type 2 achieved an accuracy of 0.3182 and F1 score of 0.2071. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.2716, 0.7323 respectively; and the F1 score were 0.1709, 0.6437 respectively.
The application of entire panorama image data for supervised with classification by heuristics grouping with ± 3years of deviation for supervised learning models and demonstrated satisfactory outcome for the age estimation.
摘要：
背景：准确的年龄估计对于临床和法医学目的至关重要。随着人工智能（AI）技术的快速发展，依靠牙齿发育的传统方法，虽然可靠，可以通过利用深度学习来增强，特别是神经网络。这项研究通过将整个全景图像用于年龄估计来评估AI模型的效率。通过监督学习(SL)模型分析结果表现。
方法：将27,877张5至90岁的牙科全景图像按2种类型进行分类。在类型1中，它们按每个年龄分类，在类型2中，应用启发式分组，20岁以上的年龄每5年分类一次.宽ResNet(WRN)和DenseNet(DN)用于监督学习。此外,在这两种类型中进行了±3年偏差的分析.
结果：对于DN模型，1型分组的准确率为0.1016,F1评分为0.058,2型分组的准确率为0.3146,F1评分为0.2027.结合±3年的偏差，1型和2型的准确性分别为0.281、0.7323；F1评分分别为0.1768、0.6583。对于WRN模型，1型分组的准确率为0.1041,F1评分为0.0599,2型分组的准确率为0.3182,F1评分为0.2071.结合±3年的偏差，1型和2型的准确性分别为0.2716，0.7323；F1评分分别为0.1709，0.6437。
结论：将整个全景图像数据用于通过启发式分类进行监督分类，对监督学习模型进行±3年的偏差分组，并证明了年龄估计的令人满意的结果。
方法：将27,877张5至90岁的牙科全景图像按2种类型进行分类。在类型1中，它们按每个年龄分类，在类型2中，应用启发式分组，20岁以上的年龄每5年分类一次.宽ResNet(WRN)和DenseNet(DN)用于监督学习。此外,在这两种类型中进行了±3年偏差的分析.
结果：对于DN模型，1型分组的准确率为0.1016,F1评分为0.058,2型分组的准确率为0.3146,F1评分为0.2027.结合±3年的偏差，1型和2型的准确性分别为0.281、0.7323；F1评分分别为0.1768、0.6583。对于WRN模型，1型分组的准确率为0.1041,F1评分为0.0599,2型分组的准确率为0.3182,F1评分为0.2071.结合±3年的偏差，1型和2型的准确性分别为0.2716，0.7323；F1评分分别为0.1709，0.6437。
结论：将整个全景图像数据用于通过启发式分类进行监督分类，对监督学习模型进行±3年的偏差分组，并证明了年龄估计的令人满意的结果。
