KneeForce: Longitudinal Knee Osteoarthritis X-ray Prediction via Diffusion Forcing with Temporal Relational Alignment and Trajectory Selection

Knee Osteoarthritis (KOA) is a serious chronic disease leading to disability, and anticipating its progression is essential for timely, personalized intervention. Existing prediction methods largely forecast non-image indicators such as future Kellgren-Lawrence (KL) grades or pain scores. However, these scalar outcomes provide only coarse, subjective assessments and lack the fine-grained visual details that clinicians need to guide treatment. Directly predicting future radiographs offers a more objective and interpretable alternative. Yet longitudinal forecasting remains challenging. Existing generative approaches struggle to maintain anatomical consistency over long horizons due to error accumulation and rigid inference. Moreover, they overlook the irregular sampling intervals inherent to clinical follow-up. To overcome these challenges, we propose KneeForce, a novel framework that adapts Diffusion Forcing for continuous disease trajectory modeling. Our method explicitly encodes the time interval to accommodate unevenly sampled inputs, and incorporates Temporal Relational Alignment, which leverages a DINO-based knee foundation model as a semantic expert to guide the generative process toward high-level disease patterns rather than mere texture synthesis. Additionally, a Trajectory Selection strategy is carefully designed to filter stochastic outputs, identifying the most clinically probable progression path. Extensive validation on the public OAI dataset and an external in-house dataset demonstrates that KneeForce consistently outperforms state-of-the-art medical and video synthesis methods in both radiographic fidelity and clinical grading accuracy. A blinded study by experienced clinicians further confirms the clinical reliability of our predictions, offering trustworthy visual evidence to support long-term prognosis.