Abstract #4621

Can infrapatellar fat pad predict the incidence of knee osteoarthritis by using deep learning based on MRI? Data from osteoarthritis initiative

Keyan Yu^1,2, Chuanyang Zheng³, Jiaping Hu¹, Lijie Zhong¹, Xiaodong Zhang¹, and Qi Dou³

¹Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China, ²Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China, ³Department of Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong, China

Synopsis

Infrapatellar fat pad (IPFP) is an important risk factor for the incident of radiographic knee osteoarthritis (iROA)^{1, 2}. However, the potential of being an independent biomarker to predict iROA is untapped. Deep learning (DL) is a set of algorithms that enable computers to discover complicated patterns in large data sets3. In this study, we train a DL model to predict iROA with auto-segmented IPFP, comparing it to the DL model set up with corresponding whole knee MR images (MRI). The results reveal that IPFP alteration can predict iROA independently comparably to the whole knee MRI at one year before iROA.

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