Osteoarthritis (OA) is the leading cause of disability among adults in the United States. As the diagnosis is based on the accurate interpretation of knee radiographs, use of a convolutional neural network (CNN) to grade OA severity has the potential to significantly reduce variability.
Knee radiographs from consecutive patients presenting to a large academic arthroplasty practice were obtained retrospectively. These images were rated by 4 fellowship-trained knee arthroplasty surgeons using the International Knee Documentation Committee (IKDC) scoring system. The intraclass correlation coefficient (ICC) for surgeons alone and surgeons with a CNN that was trained using 4755 separate images were compared.
Two hundred eighty-eight posteroanterior flexion knee radiographs (576 knees) were reviewed; 131 knees were removed due to poor quality or prior TKA. Each remaining knee was rated by 4 blinded surgeons for a total of 1780 human knee ratings. The ICC among the 4 surgeons for all possible IKDC grades was 0.703 (95% confidence interval [CI] 0.667-0.737). The ICC for the 4 surgeons and the trained CNN was 0.685 (95% CI 0.65-0.719). For IKDC D vs any other rating, the ICC of the 4 surgeons was 0.713 (95% CI 0.678-0.746), and the ICC of 4 surgeons and CNN was 0.697 (95% CI 0.663-0.73).
A CNN can identify and classify knee OA as accurately as a fellowship-trained arthroplasty surgeon. This technology has the potential to reduce variability in the diagnosis and treatment of knee OA.