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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) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe2022101361849 |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers,
2022
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| Publish Date: | 2022-10-13 |
| Description: |
AbstractAccurate 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 https://github.com/MIPT-Oulu/OAProgressionMR. see all
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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 |
| OADOI: | https://oadoi.org/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 |
| Subjects: | |
| 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. |
| Copyright information: |
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