A Deep Learning Method for Identifying Predictors of Knee Osteoarthritis Radiographic Progression From Baseline MRI

Abstract -- Background --The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs. -- Methods --Using data from the Osteoarthritis Initiative database (OAI), we implemented a Deep Learning method to predict, from baseline magnetic resonance images, further cartilage degradation, the latter being measured by Joint Space Narrowing at 12 months. -- Results --Using COR IW TSE images, our classification model achieved a ROC AUC score of 65% to be compared with a ROC AUC score of 58.7% obtained by trained radiologists. Additional analyses conducted in parallel to predict pain grade evaluated by the WOMAC pain index achieved a ROC AUC score of 72%. Attention maps provided evidence for distinct specific areas as being relevant in those two predictive models, including the internal femoro-tibial compartment for JSN progression and the intra-articular space for pain prediction. -- Conclusions --This feasibility study demonstrates the interest of deep learning applied to OA, with a potential to support even trained radiologists in the challenging task of identifying patients with a high-risk of disease progression.