Predicting Knee Osteoarthritis Pain Severity through A Deep Hybrid Learning Model: Data from the Osteoarthritis Initiative

Knee pain is the most common disabling symptom in osteoarthritis (OA). High correlation between knee pain and multiple OA features is reported in literature, but it has not been validated using deep learning models. In this study, we aim to develop a deep hybrid learning model for pain prediction directly from radiography images. We obtain an optimal hybrid model with VGG16, GAP, and KNN combination that gave a maximum of 89.75% accuracy and 0.91 Cohen’s kappa scores. The pain prediction of our proposed approach has achieved 0.99 of receiver operating characteristic area under curve (ROC-AUC). Binary pain classification has demonstrated better precision-recall curve pattern as compared to 11-class, 4-class, and 3-class pain prediction tasks. Based on Gradient-weighted Class Activation Mapping (GradCAM) analysis, joint center was identified as a key area that significantly contributes to the network’s decision-making process. The results of this study demonstrate the capability of hybrid deep learning model in predicting baseline pain severity from plain radiographs, therefore improving future OA pain assessment efforts.