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arXiv · 2201.10849

Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning

Egor Panfilov·Simo Saarakkala·Miika T. Nieminen·Aleksei Tiulpin
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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.

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Egor Panfilov, Simo Saarakkala, Miika T. Nieminen, Aleksei Tiulpin. 2022-01-26. Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning. https://doi.org/10.1109/isbi52829.2022.9761458

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