Superiority of Multiple-Joint Space Width over Minimum-Joint Space Width Approach in the Machine Learning for Radiographic Severity and Knee Osteoarthritis Progression

We compared the prediction efficiency of the multiple-joint space width (JSW) and the minimum-JSW on knee osteoarthritis (KOA) severity and progression by using a deep learning approach. A convolutional neural network (CNN) with ResU-Net architecture was developed for knee X-ray imaging segmentation and has attained a segmentation efficiency of 98.9% intersection over union (IoU) on the distal femur and proximal tibia. Later, by leveraging the image segmentation, the minimum and multiple-JSWs in the tibiofemoral joint were estimated and then validated by radiologist measurements in the Osteoarthritis Initiative (OAI) dataset using Pearson correlation and Bland–Altman plots. The agreement between the CNN-based estimation and radiologist’s measurement of minimum-JSWs reached 0.7801 (p < 0.0001). 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 64-point multiple-JSWs achieved the best performance in predicting KOA progression within 48 months, with the area-under-receiver operating characteristic curve (AUC) of 0.621, outperforming the commonly used minimum-JSW with 0.554 AUC. We provided a fully automated radiographic assessment tool for KOA with comparable performance to the radiologists and showed that the fine-grained measurement of multiple-JSWs yields superior prediction performance for KOA over the minimum-JSW.

Associated factors with the severity of hip involvement in spondyloarthritis and efficacy of TNF α inhibitors in these patients

Introduction: Hip involvement in patients with spondyloarthritis is responsible for disability and functional impairment. Its treatment is not codified. Our study aimed to determine the associated factors with moderate and severe hip involvement in spondyloarthritis patients. It also aimed to assess the efficacy of tumour necrosis factor inhibitors (TNFi) on hip disease. Methods: We conducted a cross-sectional study, including 44 spondyloarthritis patients with hip involvement. Hip involvement was diagnosed based on radiographic findings. We assessed the following parameters: Bath Ankylosing Spondylitis Metrology Index (BASMI), Bath Ankylosing Spondylitis Radiology Index (BASRI), patient global assessment (PGA), and Lequesne index. We compared these parameters and the mean radiographic joint space width between the time of the study to those right before the use of TNFi. Results: Hip involvement was bilateral in 31 patients. The mean age was 44.56±12.21 years. There were 29 men. Severe and moderate involvement (BASRI-hip>3) was reported in 21 hips from 75 affected. These patients were older and had longer diagnosis delay than patients with BASRI-hip<3. They had a higher body mass index and more limited spine mobility (BASMI). Functional hip impairment assessed by the Lequesne index was higher in these patients. TNFi prescribed in 23 patients with hip involvement, led to an improvement in the Lequesne index (12.75vs7.5,p:0.001) and PGA (7vs2,p:0.001). However, the mean joint space width remained unchanged (3.8vs3.7mm,p:0.532). Conclusion : Our study showed that higher body mass and Lequesne indexes are associated with moderate and severe hip involvement. TNFi may improve both the Lequesne index and PGA and stabilize the radiological findings.