Identity-Consistent Diffusion Network for Grading Knee Osteoarthritis Progression in Radiographic Imaging
This work addresses the need for multifaceted prognosis in knee osteoarthritis treatment, offering a more comprehensive grading approach for medical professionals, though it is incremental in improving existing generative methods.
The study tackled the problem of grading knee osteoarthritis progression in radiographic imaging by proposing a novel generative model that predicts future knee X-ray scans from baseline scans, achieving effectiveness as demonstrated on the OAI dataset.
Knee osteoarthritis (KOA), a common form of arthritis that causes physical disability, has become increasingly prevalent in society. Employing computer-aided techniques to automatically assess the severity and progression of KOA can greatly benefit KOA treatment and disease management. Particularly, the advancement of X-ray technology in KOA demonstrates its potential for this purpose. Yet, existing X-ray prognosis research generally yields a singular progression severity grade, overlooking the potential visual changes for understanding and explaining the progression outcome. Therefore, in this study, a novel generative model is proposed, namely Identity-Consistent Radiographic Diffusion Network (IC-RDN), for multifaceted KOA prognosis encompassing a predicted future knee X-ray scan conditioned on the baseline scan. Specifically, an identity prior module for the diffusion and a downstream generation-guided progression prediction module are introduced. Compared to conventional image-to-image generative models, identity priors regularize and guide the diffusion to focus more on the clinical nuances of the prognosis based on a contrastive learning strategy. The progression prediction module utilizes both forecasted and baseline knee scans, and a more comprehensive formulation of KOA severity progression grading is expected. Extensive experiments on a widely used public dataset, OAI, demonstrate the effectiveness of the proposed method.