University of Oulu

E. Panfilov, S. Saarakkala, M. T. Nieminen and A. Tiulpin, "Predicting Knee Osteoarthritis Progression from Structural MRI Using Deep Learning," 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022, pp. 1-5, doi: 10.1109/ISBI52829.2022.9761458.

Predicting knee osteoarthritis progression from structural MRI using deep learning

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Author: Panfilov, Egor1; Saarakkala, Simo1,2; Nieminen, Miika T.1,2;
Organizations: 1University of Oulu, Oulu, Finland
2Oulu University Hospital, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-10-13


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 ± 0.03 and ROC AUC of 0.78 ± 0.01. This paper sets a baseline on end-to-end KOA progression prediction from structural MRI. Our code is publicly available at

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Series: IEEE International Symposium on Biomedical Imaging
ISSN: 1945-7928
ISSN-E: 1945-8452
ISSN-L: 1945-7928
ISBN: 978-1-6654-2923-8
ISBN Print: 978-1-6654-2924-5
Article number: 9761458
DOI: 10.1109/isbi52829.2022.9761458
Host publication: 2022 IEEE 19th international symposium on biomedical imaging (ISBI)
Conference: IEEE International Symposium on Biomedical Imaging
Type of Publication: A4 Article in conference proceedings
Field of Science: 217 Medical engineering
3126 Surgery, anesthesiology, intensive care, radiology
Funding: The authors acknowledge the strategic funding of Infotech, University of Oulu and Finnish Center for Artificial Intelligence, and the computational resources by CSC – IT Center for Science, Finland. Huy Hoang Nguyen is kindly acknowledged for a discussion on Transformers. The authors have no relevant financial or non-financial interests to disclose. The OAI is a public-private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation,GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health.
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