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AI detection of knee joint effusion from radiographs: Comparative accuracy of two commercial algorithms
Jarno T Huhtanen1,2, Mikko Nyman3, Roberto Blanco Sequeiros3
1Faculty of Health and Well-being, Turku University of Applied Sciences, Finland.
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.
