biostudies-literatureYasin Pthe Health Care and Medical Research Special Project of the Xinjiang Uygur Autonomous Region38014https://www.ebi.ac.uk/biostudies/studies/S-EPMC12575809biostudies-literatureUnknown15(1)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 automatScientific reportsDual-center study on AI-driven multi-label deep learning for X-ray screening of knee abnormalities.PMC12575809BL202460Luan HYakufu MAbulimiti AYasin PPeng CYimit YSong XfalseDual-center study on AI-driven multi-label deep learning for X-ray screening of knee abnormalities.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 automat2025-01-01T00:00:00Z2025 Oct2026-06-05T09:09:34.128Z2026-05-15T03:12:14.583ZS-EPMC125758094116826210.1038/s41598-025-21895-6