Feasibility of YOLOX computer model-based assessment of knee function compared with manual assessment for people with severe knee osteoarthritis

Abstract

Abstract Objective This study aimed to assess the feasibility of computer model-based evaluation of knee joint functional capacity in comparison with manual assessment. Methods This study consisted of two phases: (1) developing an automatic knee joint action recognition and classification system on the basis of improved YOLOX and (2) analyzing the feasibility of assessment by the software system and doctors, identifying the knee joint function of patients, and determining the accuracy of the software system. We collected 40–50 samples for use in clinical experiments. The datasets used in this study were collected from patients admitted to the Joint Surgery Center. In this study, the knee joint assessment items included stair climbing, walking on uneven surfaces, and knee joint function. To assess the computer model’s automatic evaluation of knee joint function, MedCalc 20 statistical software was used to analyze the consistency of the Lequesne functional index between the computer model’s automated determinations and manual independent assessments. Results The weighted kappa coefficients between the doctors’ assessments and the software system’s assessments were 0.76 (95% confidence intervals:0.59 ~ 0.92) for climbing up and down stairs, 0.64 (95% confidence intervals:0.45 ~ 0.82) for walking on uneven floors, and 0.68 (95% confidence intervals:0.53 ~ 0.84) for the Lequesne functional index, indicating good consistency between the assessments of the software system and doctors. Conclusion This paper introduces an automatic knee joint action recognition and classification method based on improved YOLOX. By comparing the results obtained by orthopedic doctors and the software system, the feasibility of this software system was validated in the clinic.

Keywords

• Deep learning
• Knee
• Function
• Assessment