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

## TL;DR

This paper introduces a multi-modal machine learning model that predicts knee osteoarthritis progression using radiographs, clinical data, and medical history, achieving better accuracy than traditional methods and aiding drug development and personalized treatment.

## Contribution

The study presents a novel multi-modal ML approach combining radiographic and clinical data for OA progression prediction, validated on a large independent dataset.

## Key findings

- Achieved AUC of 0.79 and AP of 0.68 in predicting OA progression.
- Outperformed logistic regression baseline with higher AUC and AP.
- Potential to improve subject selection for clinical trials and personalized therapies.

## Abstract

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.