{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["23(1)"],"submitter":["Schiratti JB"],"funding":["Servier"],"pubmed_abstract":["<h4>Background</h4>The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs.<h4>Methods</h4>Using 9280 knee magnetic resonance (MR) images (3268 patients) from the Osteoarthritis Initiative (OAI) database , we implemented a deep learning method to predict, from MR images and clinical variables including body mass index (BMI), further cartilage degradation measured by joint space narrowing at 12 months.<h4>Results</h4>Using COR IW TSE images, our classification model achieved a ROC AUC score of 65%. On a similar task, trained radiologists obtained a ROC AUC score of 58.7% highlighting the difficulty of the classification task. Additional analyses conducted in parallel to predict pain grade evaluated by the WOMAC pain index achieved a ROC AUC score of 72%. Attention maps provided evidence for distinct specific areas as being relevant in those two predictive models, including the medial joint space for JSN progression and the intra-articular space for pain prediction.<h4>Conclusions</h4>This feasibility study demonstrates the interest of deep learning applied to OA, with a potential to support even trained radiologists in the challenging task of identifying patients with a high-risk of disease progression."],"journal":["Arthritis research & therapy"],"pagination":["262"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8521982"],"repository":["biostudies-literature"],"pubmed_title":["A deep learning method for predicting knee osteoarthritis radiographic progression from MRI."],"pmcid":["PMC8521982"],"pubmed_authors":["Lalande A","Pueyo M","Moingeon P","Gabarroca C","Guillier R","Dubois R","Wainrib G","Clozel T","Herent P","Schiratti JB","Dachary J","Cahane D","Keime-Guibert F"],"additional_accession":[]},"is_claimable":false,"name":"A deep learning method for predicting knee osteoarthritis radiographic progression from MRI.","description":"<h4>Background</h4>The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs.<h4>Methods</h4>Using 9280 knee magnetic resonance (MR) images (3268 patients) from the Osteoarthritis Initiative (OAI) database , we implemented a deep learning method to predict, from MR images and clinical variables including body mass index (BMI), further cartilage degradation measured by joint space narrowing at 12 months.<h4>Results</h4>Using COR IW TSE images, our classification model achieved a ROC AUC score of 65%. On a similar task, trained radiologists obtained a ROC AUC score of 58.7% highlighting the difficulty of the classification task. Additional analyses conducted in parallel to predict pain grade evaluated by the WOMAC pain index achieved a ROC AUC score of 72%. Attention maps provided evidence for distinct specific areas as being relevant in those two predictive models, including the medial joint space for JSN progression and the intra-articular space for pain prediction.<h4>Conclusions</h4>This feasibility study demonstrates the interest of deep learning applied to OA, with a potential to support even trained radiologists in the challenging task of identifying patients with a high-risk of disease progression.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2025-04-04T19:45:11.503Z","creation":"2025-04-04T19:45:11.503Z"},"accession":"S-EPMC8521982","cross_references":{"pubmed":["34663440"],"doi":["10.1186/s13075-021-02634-4"]}}