Abstract
Objectives
To develop a deep convolutional neural network (CNN) for the segmentation of femur and tibia on plain x-ray radiographs, hence enabling an automated measurement of joint space width (JSW) to predict the severity and progression of knee osteoarthritis (KOA).
Methods
A CNN with ResU-Net architecture was developed for knee X-ray imaging segmentation. The efficiency was evaluated by the Intersection over Union (IoU) score by comparing the outputs with the annotated contour of the distal femur and proximal tibia. By leveraging imaging segmentation, the minimal and multiple JSWs in the tibiofemoral joint were estimated and then validated by radiologists’ measurements in the Osteoarthritis Initiative (OAI) dataset using Pearson correlation and Bland–Altman plot. The estimated JSWs were deployed to predict the radiographic severity and progression of KOA defined by Kellgren-Lawrence (KL) grades using the XGBoost model. The classification performance was assessed using F1 and area under receiver operating curve (AUC).
Results
The network has attained a segmentation efficiency of 98.9% IoU. Meanwhile, the agreement between the CNN-based estimation and radiologist’s measurement of minimal JSW reached 0.7801 (p < 0.0001). Moreover, the 32-point multiple JSW obtained the highest AUC score of 0.656 to classify KL-grade of KOA. Whereas the 64-point multiple JSWs achieved the best performance in predicting KOA progression defined by KL grade change within 48 months, with AUC of 0.621. The multiple JSWs outperform the commonly used minimum JSW with 0.587 AUC in KL-grade classification and 0.554 AUC in disease progression prediction.
Conclusion
Fine-grained characterization of joint space width of KOA yields comparable performance to the radiologist in assessing disease severity and progression. We provide a fully automated and efficient radiographic assessment tool for KOA.
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