RÉSUMÉ
Objective To use the deep learning methods to extract features of the 1st to 7th adult costal cartilage CT reconstruction images to realize the automatic estimation of adult costal cartilage bone age.Methods 625 male and 625 female samples aged between 20 and 70 years old were collected retrospectively,and the corresponding VRT images were reconstructed by volume rendering technology(VRT).After image preprocessing and data augmentation,500 cases were used as the training set and 125 cases as the test set.The performance of ResNet,ResNeXt,DenseNet and GoogleNet networks was evaluated by using 5-fold cross-validation,and the average value of 5-fold cross-validation results was taken as the final estimation result.Results The ResNet50 network achieved the best results in both male and female datasets.The mean absolute error was 4.56 years and 3.91 years,the accuracy rate was 64.00%and 70.88%in the range of±5.0 years,88.96%and 94.40%in the range of±10.0 years,respectively.Conclusion Compared with traditional methods and machine learning methods,the deep learning models can avoid the influence of human factors,greatly improve the accuracy of adult costal cartilage bone age estimation,and reduce the error between predicted age and real age,which has high clinical application value.