  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.
