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Abstract #2957

Grading of Femoral and Tibial Cartilage Injuries in Knee Joint Based on MRI Radiomics Features

Yan Wang1, Ran Zhang2, Jie Chen1, Yusufu Rouzi1, Hui Tian1, Aihemaiti Tuohuti1, Xiaoyun Liang3, Remila Yushan1, Linsi Li1, Zhen Liu1, and Yue Wu1
1People’s Hospital of Xin jiang Uygur Autonomous Region, Xinjiang Uygur Autonomous Region, China, 2Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd., Shenyang, China, 3Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd., Shanghai, China

Synopsis

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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Keywords