Comparison of Three CNN Models Applied in Bone Age Assessment of Pelvic Radiographs of Adolescents / 法医学杂志

Objective To compare the performance of three deep-learning models （VGG19, Inception-V3 and Inception-ResNet-V2） in automatic bone age assessment based on pelvic X-ray radiographs. Methods A total of 962 pelvic X ray radiographs taken from adolescents （481 males, 481 females） aged from 11.0 to 21.0 years in five provinces and cities of China were collected, preprocessed and used as objects of study. Eighty percent of these X ray radiographs were divided into training set and validation set with random sampling method and used for model fitting and hyper-parameters adjustment. Twenty percent were used as test sets, to evaluate the ability of model generalization. The performances of the three models were assessed by comparing the root mean square error （RMSE）, mean absolute error （MAE） and Bland-Altman plots between the model estimates and the chronological ages. Results The mean RMSE and MAE between bone age estimates of the VGG19 model and the chronological ages were 1.29 and 1.02 years, respectively. The mean RMSE and MAE between bone age estimates of the Inception-V3 model and the chronological ages were 1.17 and 0.82 years, respectively. The mean RMSE and MAE between bone age estimates of the Inception-ResNet-V2 model and the chronological ages were 1.11 and 0.84 years, respectively. The Bland-Altman plots showed that the mean value of differences between bone age estimates of Inception-ResNet-V2 model and the chronological ages was the lowest. Conclusion In the automatic bone age assessment of adolescent pelvis, the Inception-ResNet-V2 model performs the best while the Inception-V3 model achieves a similar accuracy as VGG19 model.

Research Progress and Prospect of Machine Learning in Bone Age Assessment / 法医学杂志

Bone age assessment has always been one of the key issues and difficulties in forensic science. With the gradual development of machine learning in many industries, it has been widely introduced to imageology, genomics, oncology, pathology, surgery and other medical research fields in recent years. The reason why the above research fields can be closely combined with machine learning, is because the research subjects of the above branches of medicine belong to the computer vision category. Machine learning provides unique advantages for computer vision research and has made breakthroughs in medical image recognition. Based on the advantages of machine learning in image recognition, it was combined with bone age assessment research, in order to construct a recognition model suitable for forensic skeletal images. This paper reviews the research progress in bone age assessment made by scholars at home and abroad using machine learning technology in recent years.