CS-LDM: Clinically Guided Structure-Aware Latent Diffusion for Knee Radiographic Forecasting

Abstract

Knee osteoarthritis (KOA) is a leading cause of physical disability and currently has no curative clinical treatment. Early diagnosis and accurate prognosis are therefore vital for managing its progression. Existing methods mainly predict future severity grades and provide limited visual explanation of structural change. Recent generation-guided approaches synthesize follow-up radiographs for progression assessment, but generated images are often judged by predictive accuracy or image realism, so they may satisfy a grading model while remaining morphologically unreliable. We therefore propose CS-LDM, a clinically guided, structure-aware latent diffusion framework that forecasts patient-specific future knee radiographs from the current X-ray and clinically available current KL grade, using LoRA adaptation, current-grade conditioning, and frozen-teacher diagnostic distillation. Experiments on the public Osteoarthritis Initiative (OAI) dataset show that CS-LDM improves future severity-grade consistency while maintaining competitive image fidelity. Further ordinal, OARSI, ROI-level, and stable/progressed analyses show that radiographic forecasting should be evaluated by balancing diagnostic consistency, anatomical plausibility, and structural preservation.