Automatic Grading of Ankle Osteoarthritis Based on Takakura Staging System: A Deep Learning- Based Approach. Issue 4 (16th October 2019)

• Record Type: Journal Article
• Authors: Kim, Hwiyoung; Choi, Jiyun; Jang, Chul-Young; Lee, Jin Woo; Kim, Sungjun; Han, Seung Hwan
• Is Part Of: Foot & ankle orthopaedics. Volume 4:Issue 4(2019)
• Publication Date: 2019-10-16
• Subjects: ankle osteoarthritis -- Takakura staging -- Deep-Learning -- Artificial Intelligence; Foot -- Surgery -- Periodicals; Ankle -- Surgery -- Periodicals; Orthopedics -- Periodicals; 617.584
• DOI: 10.1177/2473011419S00246
• Languages: English
• ISSNs: 2473-0114

Abstract

Category: Ankle, Ankle Arthritis Introduction/Purpose: The Takakura staging system has been used for the stratification in ankle osteoarthritis(OA). Patient's OA stage is determined by visual examination on the status of talar and distal tibia in anteroposterior ankle radiograph. Clinical decisions about whether to treat conservatively or to treat with operation such as supra-malleolar osteotomy or arthroplasty may depend on this grading system. However, this is not completely reproducible between examiners and it makes a debating situation about different treatment method to a same ankle radiograph. If highly reproducible measurement method may be suggested this debating may no longer need. Therefore, the purpose of this study was to suggest a deep learning-based algorithm that automatically grades ankle osteoarthritis and to present feasibility of the provided automatic grading system. Methods: 2529 AP both-ankle radiographs were collected and graded for OA by a radiologist and orthopedic surgeon. We converted Takakura staging system into 3 grades(Grade1: stageI, Grade2: II-IIIa, Grade3: IIIb-IV) according to treatment plan. To confine the region of interest(ROI), a rectangle encompassing ankle portion was automatically generated using an object detection model(YOLOv2). The data oversampling was done to overcome small data and class imbalance. Four pre-trained convolutional networks(One Inception-v3 and three ResNet models) were fine-tuned using augmented data. We tried two different ensemble methods: voting ensemble and gradient boosting. Voting ensemble adjusts the decision through selecting majority votes among trained models. Gradient boosting(XGboost model) trains new classification model to focus on the cases that previous model mis- classified. The evaluation of trained models and ensemble model were performed in terms of average classification accuracy. Gradient-class activation map(CAM) method was utilized to present CAM highlighting the location where highly affected the network for the decision. Results: A total of 3836 original ROIs were obtained and as follows: grade 1, 1382; grade 2, 1927; grade 3, 527. The number of oversampled ROIs was 16398 like follows: grade 1, 5528; grade 2, 7708; grade 3, 3162. The performance of each classifier was ranged 71.0% ˜ 77.3% in terms of average classification accuracy. Ensemble methods yielded average classification accuracies of 78.1% and 79.2% for voting ensemble and XGboost, respectively. Conclusion: Deep learning-based algorithm application for automatic grading of ankle osteoarthritis based on Takakura staging system is feasible. This approach is expected to be applied to various staging system for arthritis assessment through radiographs.