Accuracy of Key Sonographic Markers for Juxta‐Articular Fractures
Xiaofang Fu, Lirong Liu, Hao Sun, Changqin Sun, Qinhan Yang, Mengjie Dou, Duo Shi, Junpu Hu, Zhiming He, Guangquan Zhou, Shiyu Jin, Faqin LvObjectives
To systematically evaluate sonographic features of long bone juxta‐articular fractures and identify key diagnostic predictors using machine learning‐based feature selection.
Methods
This prospective single‐center diagnostic accuracy study enrolled 121 patients with clinically suspected juxta‐articular fractures. Ten predefined sonographic features were assessed, with computed tomography (CT) serving as the reference standard. Key features were identified using shapley additive explanations (SHAP), and the 3 top‐ranked features were used to construct a decision tree–based sonographic triad model, hereafter referred to as the sonographic triad model. This model generated a final binary ultrasound diagnosis for each suspected fracture site. Agreement between the final binary ultrasound diagnosis and CT was quantified using Cohen's κ with 95% confidence intervals (CIs). Inter‐observer and intra‐observer agreement was evaluated using blinded dual‐reader review across the cohort. Diagnostic performance was quantified by sensitivity, specificity, predictive values, and the area under the receiver operating characteristic curve (AUC).
Results
Analysis of 209 suspected juxta‐articular fracture sites revealed that subcutaneous soft‐tissue edema (88.5%), cortical separation (50.7%), and heterogeneous intra‐articular effusion (56.9%) were the most prevalent findings. The SHAP analysis identified a specific predictive triad. The 3 primary predictors were subperiosteal hematoma (43.8% contribution), arc‐shaped fracture end (28.3%), and heterogeneous intra‐articular effusion (26.6%). These 3 features collectively accounted for 98.7% of the total feature importance and demonstrated high reproducibility, with good inter‐observer agreement (κ = 0.833–0.980) and excellent intra‐observer agreement (κ = 0.987–1.000). The final binary ultrasound diagnosis generated by the sonographic triad model demonstrated near‐perfect agreement with CT at the suspected fracture‐site level (κ = 0.904, 95% CI: 0.847–0.962). In addition, the sonographic triad model achieved excellent diagnostic performance, with a sensitivity of 93.0%, specificity of 97.2%, positive predictive value (PPV) of 96.8%, negative predictive value (NPV) of 93.8%, and an AUC of 0.966.
Conclusions
The sonographic triad model, based on subperiosteal hematoma, arc‐shaped fracture end, and heterogeneous intra‐articular effusion, demonstrated high diagnostic accuracy and strong concordance with CT for suspected juxta‐articular long‐bone fractures. This radiation‐free and rapid approach may assist early screening and triage.