{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yasin P"],"funding":["the Health Care and Medical Research Special Project of the Xinjiang Uygur Autonomous Region"],"pagination":["38014"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12575809"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["Knee abnormalities, such as meniscus tears and ligament injuries, are common in clinical practice and pose significant diagnostic challenges. While traditional imaging techniques-X-ray, Computed Tomography (CT) scan, and Magnetic Resonance Imaging (MRI)-are vital for assessment. However, X-rays and CT scans often fail to adequately visualize soft tissue injuries, and MRIs can be costly and time-consuming. To overcome these limitations, we developed an innovative AI-driven approach that allows for the detection of soft tissue abnormalities directly from X-ray images-a capability traditionally reserved for MRI or arthroscopy. We conducted a retrospective study with 4,215 patients from two medical centers, utilizing knee X-ray images annotated by orthopedic surgeons. The YOLOv11 model automat"],"journal":["Scientific reports"],"pubmed_title":["Dual-center study on AI-driven multi-label deep learning for X-ray screening of knee abnormalities."],"pmcid":["PMC12575809"],"funding_grant_id":["BL202460"],"pubmed_authors":["Luan H","Yakufu M","Abulimiti A","Yasin P","Peng C","Yimit Y","Song X"],"additional_accession":[]},"is_claimable":false,"name":"Dual-center study on AI-driven multi-label deep learning for X-ray screening of knee abnormalities.","description":"Knee abnormalities, such as meniscus tears and ligament injuries, are common in clinical practice and pose significant diagnostic challenges. While traditional imaging techniques-X-ray, Computed Tomography (CT) scan, and Magnetic Resonance Imaging (MRI)-are vital for assessment. However, X-rays and CT scans often fail to adequately visualize soft tissue injuries, and MRIs can be costly and time-consuming. To overcome these limitations, we developed an innovative AI-driven approach that allows for the detection of soft tissue abnormalities directly from X-ray images-a capability traditionally reserved for MRI or arthroscopy. We conducted a retrospective study with 4,215 patients from two medical centers, utilizing knee X-ray images annotated by orthopedic surgeons. The YOLOv11 model automat","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-06-05T09:09:34.128Z","creation":"2026-05-15T03:12:14.583Z"},"accession":"S-EPMC12575809","cross_references":{"pubmed":["41168262"],"doi":["10.1038/s41598-025-21895-6"]}}