Objective:To explore the feasibility of the AI intelligent reconstruction model based on knee joint magnetic resonance data developed by Nuctech Company Limited for evaluating knee cartilage injury.Methods:Thirty-three patients (a total of forty-one knees) who were hospitalized with severe knee osteoarthritis in Beijing Tsinghua Changgung Hospital from May 2021 to April 2022 were selected. All of them were planned to be performed total knee arthroplasty (TKA) for the treatment of knee osteoarthritis. Fifteen males with an average age of 71±5 years old and twenty six females with an average age of 71±9 years old were included in this study. There were 19 cases of left knee and 22 cases of right knee. Thin layer MRI examination on the patients' knee joints was performed before the surgery, and artificial intelligence model based on the thin layer MRI data of the knee joint was reconstructed. The cartilage part of the model was selected and corrected by Principal Component Analysis (PCA) in order to realize model straightening. The tibial plateau cartilage of knee joint which intercepted during operation was classified according to the International Cartilage Repair Society (ICRS). Finally the results were compared with the ICRS classification results of knee artificial intelligence reconstruction model and artificial recognition of knee joint MRI images.Results:Compared with the grade of cartilage injury intercepted during our operation which was according to the ICRS classification, the sensitivity of artificial intelligence reconstruction model for the diagnosis of cartilage injury with ICRS classification grade four was 93.1%. The specificity of artificial intelligence reconstruction model was 91.4%. The positive predictive value (PPV) of artificial intelligence reconstruction model was 92.2%. And the negative predictive value (NPV) of artificial intelligence reconstruction model was 80.3%. The area under ROC curve (AUC) was 0.92. The ICRS classification consistency between artificial intelligence model and physical inspection results was good with kappa value 0.81 ( P<0.001) . In the aspect of artificial recognition of cartilage injury grading in MRI images, the sensitivity of artificial recognition was 92.10% compared with the manual identification of cartilage injury classification in MRI images. The specificity of artificial recognition was 91.60%. The positive predictive value (PPV) of artificial recognition was 97.20% and the negative predictive value (NPV) of artificial recognition was 78.8%. The kappa value of the cartilage injury classification in MRI images consistency between artificial recognition and manual identification was 0.79 ( P<0.001). Conclusion:Based on the evaluation of cartilage injury by AI reconstruction model of knee joint, the sensitivity and specificity of the diagnosis of ICRS grade IV cartilage injury can be acceptable, but still needs to be improved.