A deep transfer learning method using plain radiographs for the differential diagnosis of osteonecrosis of the femoral head with other hip diseases
10.3760/cma.j.cn121113-20220831-00508
- VernacularTitle:基于深度迁移学习模型实现股骨头坏死与其他髋部疾病的X线片鉴别诊断
- Author:
Zeqing HUANG
1
;
Yuhao LIU
;
Hanjun FANG
;
Haicheng CHEN
;
Haibin WANG
;
Zhenqiu CHEN
;
Chi ZHOU
Author Information
1. 广州中医药大学第一附属医院三骨科，广州 510405
- Keywords:
Artificial intelligence;
Deep learning;
Diagnosis, differential;
Femur head necrosis
- From:
Chinese Journal of Orthopaedics
2023;43(1):72-80
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a deep transfer learning method for the differential diagnosis of osteonecrosis of the femoral head (ONFH) with other common hip diseases using anteroposterior hip radiographs.Methods:Patients suffering from ONFH, DDH, and other hip diseases including primary hip osteoarthritis, non-infectious inflammatory hip disease, and femoral neck fracture treated in the First Affiliated Hospital of Guangzhou University of Chinese Medicine from January 2018 to December 2020 were enrolled in the study. A clinical data set containing anteroposterior hip radiographs of the eligible patients was created. Data augmentation by rotating and flipping images was performed to enlarge the data set, then the data set was divided equally into a training data set and a testing data set. The ResNet-152, a deep neural network model, was used in the study, but the original Batch Normalization was replaced with Transferable Normalization to construct a novel deep transfer learning model. The model was trained to distinguish ONFH and DDH from other common hip diseases using anteroposterior hip radiographs on the training data set and its classification performance was evaluated on the testing data set.Results:The clinical data set was comprised of anteroposterior hip radiographs of 1024 hips, including 542 with ONFH, 296 with DDH, and 186 with other common hip diseases (56 hips with primary osteoarthritis, 85 hips with non-infectious inflammatory osteoarthritis, 45 hips with femoral neck fracture). After data augmentation, the size of the data set multiplied to 6144. The model was trained 100 050 times in each task. Accuracy was used as the representative parameter to evaluate the performance of the model. In the binary classification task to identify ONFH, the best accuracy was 95.80%. As for the multi-classification task for classification of ONFH and DDH from other hip diseases, the best accuracy was 91.40%. The plateau of the model was observed in each task after 50 000 times of training. The mean accuracy in plateaus was 95.35% (95% CI: 95.33%, 95.37%), and 90.85% (95% CI: 90.82%, 90.87%), respectively. Conclusion:The present study proves the encouraging performance of a deep transfer learning method for the first-visit classification of ONFH, DDH, and other hip diseases using the convenient and economical anteroposterior hip radiographs.
