Localizing Knee Pain via Explainable Bayesian Generative Models and Counterfactual MRI: Data from the Osteoarthritis Initiative.

Abstract

Osteoarthritis (OA) pain often does not correlate with magnetic resonance imaging (MRI)-detected structural abnormalities, limiting the clinical utility of traditional volume-based lesion assessments. To address this mismatch, we present a novel explainable artificial intelligence (XAI) framework that localizes pain-driving abnormalities in knee MR images via counterfactual image synthesis and Shapley-based feature attribution. Our method combines a Bayesian generative network-which is trained to synthesize asymptomatic versions of symptomatic knees-with a black-box pain classifier to generate counterfactual MRI scans. These counterfactuals, which are constrained by multimodal segmentation and uncertainty-aware inference, isolate lesion regions that are likely responsible for symptoms. Applying Shapley additive explanations (SHAP) to the output of the classifier enables the contribution of each lesion to pain to be precisely quantified. We trained and validated this framework on 2148 knee pairs obtained from a multicenter study of the Osteoarthritis Initiative (OAI), achieving high anatomical specificity in terms of identifying pain-relevant features such as patellar effusions and bone marrow lesions. An odds ratio (OR) analysis revealed that SHAP-derived lesion scores were significantly more strongly associated with pain than raw lesion volumes were (OR 6.75 vs. 3.73 in patellar regions), supporting the interpretability and clinical relevance of the model. Compared with conventional saliency methods and volumetric measures, our approach demonstrates superior lesion-level resolution and highlights the spatial heterogeneity of OA pain mechanisms. These results establish a new direction for conducting interpretable, lesion-specific MRI analyses that could guide personalized treatment strategies for musculoskeletal disorders.