Dual-center study on AI-driven multi-label deep learning for X-ray screening of knee abnormalities.

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

SUBMITTER: Yasin P 

PROVIDER: S-EPMC12575809 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature