Keywords: Cartilage, Radiomics, Femoral Cartilage; Tibial Cartilage; Injury Grading
Motivation: To create a tool that helps doctors quickly and accurately assess the severity of knee cartilage damage based on MRI images.
Goal(s): Develop a nomogram for predicting the severity of cartilage injuries in knee osteoarthritis patients.
Approach: Utilize deep learning to segment knee joint cartilage from MRI and extract radiomics features, combined with clinical variables to predict cartilage grading.
Results: The nomogram models for femoral and tibial cartilage grading showed high accuracy, with AUC values of 0.872 and 0.914 respectively, and were validated internally.
Impact: The constructed models provide a reliable method for diagnosing cartilage damage severity, assisting in designing personalized treatment plans for patients with knee osteoarthritis.
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