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

arXiv: https://arxiv.org/abs/2506.14560 · HTML: https://mlsanity.com/paper/2506.14560/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 17 Jun 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 12 of 12)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Only a Matter of Style: Age Transformation Using a Style-Based Regression Model](https://mlsanity.com/paper/2102.02754/) (2021)
- [Lifespan Age Transformation Synthesis](https://mlsanity.com/paper/2003.09764/) (2020)
- [KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks](https://mlsanity.com/paper/1907.12237/) (2019)
- [Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals](https://mlsanity.com/paper/1912.12127/) (2019)
- [Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data](https://mlsanity.com/paper/2210.13889/) (2022)
- [Individualized multi-horizon MRI trajectory prediction for Alzheimer's Disease](https://mlsanity.com/paper/2408.02018/) (2024)
- [Image Evolution Trajectory Prediction and Classification from Baseline using Learning-based Patch Atlas Selection for Early Diagnosis](https://mlsanity.com/paper/1907.06064/) (2019)
- [Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network](https://mlsanity.com/paper/2208.04825/) (2022)
- [Aging prediction using deep generative model toward the development of preventive medicine](https://mlsanity.com/paper/2208.10797/) (2022)
- [Extrapolating Prospective Glaucoma Fundus Images through Diffusion Model in Irregular Longitudinal Sequences](https://mlsanity.com/paper/2410.21130/) (2024)

## Built on by (most cited first, 1 of 1)

- [DiffSight-Former: Modeling Structural Differences and Temporal Dynamics for Glaucoma Progression Prediction](https://mlsanity.com/paper/2606.09140/) (2026)

## Abstract (arXiv)

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.