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.