AI detection of knee joint effusion from radiographs: Comparative accuracy of two commercial algorithms

Background: Knee joint effusion might indicate injury even without bony changes. Automated detection from radiographs could improve the sensitivity of AI algorithms. Purpose: To compare two commercially available AI algorithms, BoneView and RBfracture, in detecting knee joint effusion. Material and Methods: This retrospective study collected 123 lateral knee radiographs. Detection of knee joint effusion by both AI algorithms was compared with two board-certified radiologists with arbitration. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and interobserver agreement (Cohen’s Kappa) were calculated. 95% confidence intervals (CI) assessed robustness. McNemar’s tests compared sensitivity and specificity between AI algorithms. Results: Knee joint effusion was present in 56% of radiographs. BoneView demonstrated a sensitivity of 0.42 (95% CI: 0.31–0.54), specificity of 1.00 (95% CI: 0.93–1.00), PPV of 1.00 (95% CI: 0.88–1.00), NPV of 0.57 (95% CI: 0.47–0.67), and accuracy of 0.68 (95% CI: 0.59–0.75). RBfracture demonstrated a sensitivity of 0.75 (95% CI: 0.64–0.84), specificity of 0.91 (95% CI: 0.80–0.96), PPV of 0.91 (95% CI: 0.81–0.96), NPV of 0.74 (95% CI: 0.63–0.83), and accuracy of 0.82 (95% CI: 0.74–0.88). Cohen’s Kappa was 0.49 (95% CI: 0.35–0.63), indicating moderate agreement between the two AI algorithms. Adding knee joint effusion detection to fracture/dislocation predictions improved sensitivity. Conclusions: Two commercially available AI algorithms demonstrated different operating points for knee joint effusion detection: BoneView achieved high specificity, while RBfracture achieved higher sensitivity. Combining injury and effusion predictions increased sensitivity at the cost of specificity.