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Abstract #3102

Predicting Knee Osteoarthritis Progression with an Interpretable Deep Learning Approach on MRIs: Data from the Osteoarthritis Initiative

Jiaping Hu1, Chuanyang Zheng2, Lijie Zhong1, Keyan Yu1, Yanjun Chen1, Zhao Wang2, Zhongping Zhang3, Qi Dou2, and Xiaodong Zhang1
1Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University, GuangZhou, China, 2Department of Computer Science & Engineering, The Chinese University of Hong Kong, HongKong, China, 3Philips Healthcare, GuangZhou, China


Identifying knee osteoarthritis progressors is significant. MRIs can reflect the structures of the knee. However, currently no tool could rapidly and objectively predict knee osteoarthritis progression based on MRI. Therefore, we applied deep learning algorithms on MRIs of the whole knee to predict progression at three time points. The Gradient-weighted Class Activation Maps were employed for interpretability, and the highlighted infrapatellar fat pad (IPFP) was segmented for progression prediction. We showed that the deep learning framework performed well on discrimination of progressors, especially at 24th months, and that the infrapatellar fat pad plays an important role in predicting progression.

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