Mu Chuang Chuang, Li Gang
Chin J Dent Res. 2022 Jun 10;25(2):119-124. doi: 10.3290/j.cjdr.b3086341.
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.
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.
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.
Transfer learning models can extract different features in different age groups and can be used for age estimation in panoramic radiographs.
评估转移学习模型对性别和年龄分布均衡的患者恒牙列全景照片进行年龄估计的准确性，并提供一种新的年龄估计方法。
将3000例性别和年龄分布均衡的患者的全景照片分为三组：训练集（n = 2400）、验证集（n = 300）和测试集（n = 300）。使用训练集对ResNet、EffiecientNet、VggNet和DenseNet转移学习模型进行训练。随后使用测试集中的数据对模型进行测试。计算平均绝对误差，并对深度学习模型在不同年龄组中提取的不同特征进行可视化。
测试集中最优转移学习模型EfficientNet-B5的平均绝对误差（MAE）和均方根误差（RMSE）分别为2.83和4.59。牙列、上颌窦、下颌体和下颌角在年龄估计中均发挥作用。
转移学习模型可以在不同年龄组中提取不同特征，可用于全景X线片中的年龄估计。
