Deep learning and radiomics-based system for early diagnosis of hip synovitis in juvenile idiopathic arthritis.

Objective: Juvenile Idiopathic Arthritis (JIA) frequently affects children's hips, causing severe progression, but early hip synovitis lacks obvious symptoms and is hard to detect via conventional ultrasound, delaying diagnosis. magnetic resonance imaging (MRI), though accurate, is costly and inaccessible for routine use. This study aims to develop an automatic identification system for the early diagnosis of hip synovitis in JIA through the integration of deep learning and radiomics techniques. Methods: A YOLO-JIA model specifically designed for the automatic segmentation of hip ultrasound images was developed. Radiomic features were extracted from these segmented regions. Subsequently, feature selection was performed using the analysis of variance (ANOVA) test followed by least absolute shrinkage and selection operator (LASSO) regression. Based on the selected features, a Random Forest (RF) classification model was constructed and evaluated separately on an internal and an external validation set. Results: The YOLO-JIA model demonstrated high precision (0.98) and recall (1.00) in object detection tasks, with a mean average precision at 50-95% (mAP50-95) for mask (M) reaching 0.86. The RF classification model achieved an area under the curve (AUC) of 0.88 on the internal validation set and 0.81 on the external validation set. Decision curve analysis further confirmed the clinical utility of our proposed system. Finally, the models were integrated and deployed locally. Conclusion: This study successfully developed a system for the early diagnosis of JIA hip synovitis based on deep learning and radiomics. The system offers an effective and reliable means for early screening, enhancing diagnosis rates, and ultimately reducing the risk of severe joint damage in JIA patients.