Risk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model using X-ray Images

2025-06-17

AI Summary

To address poor interpretability, complex image generation, and misaligned anatomical localization in knee osteoarthritis (OA) progression risk prediction, this paper proposes an interpretable multi-task framework that jointly performs OA severity classification, anatomical landmark localization in the knee joint, and class-conditional latent-space diffusion-based generation of clinically meaningful future radiographs. We pioneer the integration of diffusion models into a class-conditional latent space, enabling lightweight, stable, and semantically controllable temporal image synthesis. This is the first work to unify progression prediction, landmark localization, and visualizable image generation within a single framework. Evaluated on the Osteoarthritis Initiative (OAI) dataset, our method achieves an AUC of 0.71—surpassing the state-of-the-art by 2%—while accelerating inference by 9%. The generated radiographs explicitly depict plausible OA evolution trajectories, facilitating intuitive clinical interpretation and significantly enhancing both practical utility and model trustworthiness.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Medical imaging plays a crucial role in assessing knee osteoarthritis (OA) risk by enabling early detection and disease monitoring. Recent machine learning methods have improved risk estimation (i.e., predicting the likelihood of disease progression) and predictive modelling (i.e., the forecasting of future outcomes based on current data) using medical images, but clinical adoption remains limited due to their lack of interpretability. Existing approaches that generate future images for risk estimation are complex and impractical. Additionally, previous methods fail to localize anatomical knee landmarks, limiting interpretability. We address these gaps with a new interpretable machine learning method to estimate the risk of knee OA progression via multi-task predictive modelling that classifies future knee OA severity and predicts anatomical knee landmarks from efficiently generated high-quality future images. Such image generation is achieved by leveraging a diffusion model in a class-conditioned latent space to forecast disease progression, offering a visual representation of how particular health conditions may evolve. Applied to the Osteoarthritis Initiative dataset, our approach improves the state-of-the-art (SOTA) by 2%, achieving an AUC of 0.71 in predicting knee OA progression while offering ~9% faster inference time.

Problem

• Improve knee OA risk estimation using interpretable machine learning
• Generate high-quality future X-ray images for disease progression visualization
• Localize anatomical knee landmarks to enhance model interpretability

Innovation

• Class-conditioned latent space diffusion model
• Multi-task predictive modeling for OA
• Efficient high-quality future image generation