Automatic knee osteoarthritis severity grading based on X-ray images using a hierarchical classification method

Abstract

Abstract Background This study aims to develop a hierarchical classification method to automatically assess the severity of knee osteoarthritis (KOA). Methods This retrospective study recruited 4074 patients. Clinical diagnostic indicators and clinical diagnostic processes were applied to develop a hierarchical classification method that involved four sub-task classifications. These four sub-task classifications were the classification of Kellgren-Lawrence (KL) grade 0–2 and KL grade 3–4, KL grade 3 and KL grade 4, KL grade 0 and KL grade 1–2, and KL grade 1 and KL grade 2, respectively. To extract the features of clinical diagnostic indicators, four U-Net models were first used to segment the total joint space (TJS), the lateral joint space (LJS), the medial joint space (MJS), and osteophytes, respectively. Based on the segmentation result of TJS, the region of knee subchondral bone was generated. Then, geometric features were extracted based on segmentation results of the LJS, MJS, TJS, and osteophytes, while radiomic features were extracted from the knee subchondral bone. Finally, the geometric features, radiomic features, and combination of geometric features and radiomic features were used to construct the geometric model, radiomic model, and combined model in KL grading, respectively. A strict decision strategy was used to evaluate the performance of the hierarchical classification method in all X-ray images of testing cohort. Results The U-Net models achieved relatively satisfying performances in the segmentation of the TJS, the LJS, the MJS, and the osteophytes with the dice similarity coefficient of 0.88, 0.86, 0.88, and 0.64 respectively. The combined models achieved the best performance in KL grading. The accuracy of combined models was 98.50%, 81.65%, 82.07%, and 74.10% in the classification of KL grade 0–2 and KL grade 3–4, KL grade 3 and KL grade 4, KL grade 0 and KL grade 1–2, and KL grade 1 and KL grade 2, respectively. For all X-ray images of the testing cohort, the accuracy of the hierarchical classification method was 65.98%. Conclusion The hierarchical classification method developed in the current study is a feasible approach to assess the severity of KOA.

Keywords

• Knee osteoarthritis
• U-Net
• Machine learning
• X-ray image