Knee osteoarthritis severity classification with ordinal regression module

Abstract

Osteoarthritis (OA) is a common form of knee arthritis which causes significant disability and is threatening to plague patient’s quality of life. Although this chronic condition does not lead to fatality, still there exists no known cure for OA. Diagnosis of OA can be confirmed primarily based on radiographic findings. Being a progressive disease, early identification of OA is crucial for clinical interventions to curtail the OA degeneration. Kellgren-Lawrence (KL) grading system has been traditionally employed to assess the knee OA severity. Due to the recent advancements of deep learning in computer vision, more studies have employed deep neural network in automatically predicting KL grade from plain knee joint radiograph. However, these studies treat KL grading as a multi-class classification task and ignore the inherent ordinal nature within the KL grades. In this study, we propose an ordinal regression module for neural networks to treat KL grading as an ordinal regression task. Our module takes an input from neural network and produces 4 cut-points to partition the prediction space into 5 respective KL grades. The proposed model is optimized by a cumulative-link loss function. Performance of the model is evaluated against various notable neural networks and significant improvements on the knee OA KL grade prediction were demonstrated.