Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning
This work addresses the problem of improving disease understanding and clinical trial support for knee osteoarthritis patients, representing an incremental advance over prior methods.
The paper tackled predicting knee osteoarthritis progression from structural MRI using a deep learning approach that combines a 2D CNN with a Transformer, achieving an average precision of 0.58±0.03 and ROC AUC of 0.78±0.01 on a cohort of 4,866 patients.
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.