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.
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。