Age Estimation using Panoramic Radiographs by Transfer Learning.

Abstract

OBJECTIVE To assess the accuracy of transfer learning models for age estimation from panoramic photographs of permanent dentition of patients with an equal sex and age distribution and provide a new method of age estimation. METHODS The panoramic photographs of 3000 patients with an equal sex and age distribution were divided into three groups: a training set (n = 2400), validation set (n = 300) and test set (n = 300). The ResNet, EffiecientNet, VggNet and DenseNet transfer learning models were trained with the training set. The models were subsequently tested using the data in the test set. The mean absolute errors were calculated and the different features extracted by the deep learning models in different age groups were visualixed. RESULTS The mean absolute error (MAE) and root mean square error (RMSE) of the optimal transfer learning model EfficientNet-B5 in the test set were 2.83 and 4.59, respectively. The dentition, maxillary sinus, mandibular body and mandibular angle all played a role in age estimation. CONCLUSION Transfer learning models can extract different features in different age groups and can be used for age estimation in panoramic radiographs.