A Discordance-Aware Multimodal Framework with Multi-Agent Clinical Reasoning
This work addresses clinical interpretation challenges in knee osteoarthritis for healthcare providers, though it represents an incremental improvement over existing decision support systems.
The researchers tackled the problem of discordance between structural damage and patient-reported symptoms in knee osteoarthritis by developing a multimodal framework that predicts progression types and computes discordance scores, achieving improved patient stratification and phenotype-specific management recommendations.
Knee osteoarthritis frequently exhibits discordance between structural damage observed in imaging and patient-reported symptoms such as pain. This mismatch complicates clinical interpretation and patient stratification and remains insufficiently modeled in existing decision support systems. We propose a discordance aware multimodal framework that combines machine learning prediction models with a tool grounded multi agent reasoning system. Using baseline data from the FNIH Osteoarthritis Biomarkers Consortium, we trained multimodal models to predict two progression tasks, joint space loss only progression versus non progression, and pain only progression versus non progression. The predictive system integrates three modality specific experts: a CatBoost tabular model using demographic, radiographic, MRI-derived scalar, and biomarker features; MRI image embeddings extracted using a ResNet18 backbone; and Xray embeddings derived from the same architecture. Expert predictions are fused using a stacking ensemble. Residual based models estimate expected pain from structural features, enabling the computation of a pain structure discordance score between observed and expected symptoms. A multi-agent reasoning layer interprets these signals to assign clinically interpretable OA phenotypes and generate phenotype specific management recommendations.