Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning

TL;DR

This paper introduces a deep learning approach combining 2D CNNs and Transformers to predict knee osteoarthritis progression from structural MRI, outperforming traditional methods and establishing a new baseline.

Contribution

It presents an end-to-end deep learning method that automatically learns features from raw MRI data for KOA progression prediction, surpassing prior biomarker-based approaches.

Findings

Achieved average precision of 0.58 and ROC AUC of 0.78.

Outperformed conventional CNN-based models.

Set a new baseline for MRI-based KOA progression prediction.

Abstract

Accurate prediction of knee osteoarthritis (KOA) progression from structural MRI has a potential to enhance disease understanding and support clinical trials. Prior art focused on manually designed imaging biomarkers, which may not fully exploit all disease-related information present in MRI scan. In contrast, our method learns relevant representations from raw data end-to-end using Deep Learning, and uses them for progression prediction. The method employs a 2D CNN to process the data slice-wise and aggregate the extracted features using a Transformer. Evaluated on a large cohort (n=4,866), the proposed method outperforms conventional 2D and 3D CNN-based models and achieves average precision of $0.58\pm0.03$ and ROC AUC of $0.78\pm0.01$. This paper sets a baseline on end-to-end KOA progression prediction from structural MRI. Our code is publicly available at https://github.com/MIPT-Oulu/OAProgressionMR.