# Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data

arXiv: https://arxiv.org/abs/1904.06236 · HTML: https://mlsanity.com/paper/1904.06236/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2019.

## Built on by (most cited first, 23 of 23)

- [Integrated multimodal artificial intelligence framework for healthcare applications](https://mlsanity.com/paper/2202.12998/) (2022)
- [A Closer Look at AUROC and AUPRC under Class Imbalance](https://mlsanity.com/paper/2401.06091/) (2024)
- [Review of multimodal machine learning approaches in healthcare](https://mlsanity.com/paper/2402.02460/) (2024)
- [Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data](https://mlsanity.com/paper/2210.13889/) (2022)
- [CLIMAT: Clinically-Inspired Multi-Agent Transformers for Knee Osteoarthritis Trajectory Forecasting](https://mlsanity.com/paper/2104.03642/) (2021)
- [Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning](https://mlsanity.com/paper/2201.10849/) (2022)
- [Multimodal Machine Learning in Precision Health](https://mlsanity.com/paper/2204.04777/) (2022)
- [Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis](https://mlsanity.com/paper/2311.06118/) (2023)
- [Image prediction of disease progression by style-based manifold extrapolation](https://mlsanity.com/paper/2111.11439/) (2021)
- [UNet Architectures in Multiplanar Volumetric Segmentation -- Validated on Three Knee MRI Cohorts](https://mlsanity.com/paper/2203.08194/) (2022)
- [Evidence-aware multi-modal data fusion and its application to total knee replacement prediction](https://mlsanity.com/paper/2303.13810/) (2023)
- [End-To-End Prediction of Knee Osteoarthritis Progression With Multi-Modal Transformers](https://mlsanity.com/paper/2307.00873/) (2023)
- [Synthesizing Bidirectional Temporal States of Knee Osteoarthritis Radiographs with Cycle-Consistent Generative Adversarial Neural Networks](https://mlsanity.com/paper/2311.05798/) (2023)
- [Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification](https://mlsanity.com/paper/2311.05799/) (2023)
- [A Progressive Risk Formulation for Enhanced Deep Learning based Total Knee Replacement Prediction in Knee Osteoarthritis](https://mlsanity.com/paper/2406.10119/) (2024)
- [Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data and Symptomatic Assessments](https://mlsanity.com/paper/2305.05927/) (2023)
- [Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review](https://mlsanity.com/paper/2308.09380/) (2023)
- [Identity-Consistent Diffusion Network for Grading Knee Osteoarthritis Progression in Radiographic Imaging](https://mlsanity.com/paper/2407.21381/) (2024)
- [Toward Cost-efficient Adaptive Clinical Trials in Knee Osteoarthritis with Reinforcement Learning](https://mlsanity.com/paper/2408.02349/) (2024)
- [Emory Knee Radiograph (MRKR) Dataset](https://mlsanity.com/paper/2411.00866/) (2024)
- [Challenges and proposed solutions in modeling multimodal medical data: A systematic review](https://mlsanity.com/paper/2505.06945/) (2025)
- [PSSF: Early osteoarthritis detection using physical synthetic knee X-ray scans and AI radiomics models](https://mlsanity.com/paper/2601.11642/) (2026)
- [A Discordance-Aware Multimodal Framework with Multi-Agent Clinical Reasoning](https://mlsanity.com/paper/2604.16333/) (2026)

## Abstract (arXiv)

Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to prevent millions of total joint replacement surgeries performed annually. Here, we present a multi-modal machine learning-based OA progression prediction model that utilizes raw radiographic data, clinical examination results and previous medical history of the patient. We validated this approach on an independent test set of 3,918 knee images from 2,129 subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78-0.81) and Average Precision (AP) of 0.68 (0.66-0.70). In contrast, a reference approach, based on logistic regression, yielded AUC of 0.75 (0.74-0.77) and AP of 0.62 (0.60-0.64). The proposed method could significantly improve the subject selection process for OA drug-development trials and help the development of personalized therapeutic plans.