Purpose To determine the feasibility of using a deep learning approach to detect cartilage
lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due …

Purpose To test the hypothesis that artificial intelligence (AI) techniques can aid in
identifying and assessing lesion severity in the cartilage, bone marrow, meniscus, and …

Background Semiquantitative assessment of MRI plays a central role in musculoskeletal
research; however, in the clinical setting MRI reports often tend to be subjective and …

Purpose We developed a tool for locating and grading knee osteoarthritis (OA) from digital X-
ray images and illustrate the possibility of deep learning techniques to predict knee OA as …

The improved treatment of knee injuries critically relies on having an accurate and cost-
effective detection. In recent years, deep-learning-based approaches have monopolized …

Background Magnetic resonance imaging (MRI) of the knee is the preferred method for
diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject …

Background MRI is a powerful diagnostic tool with a long acquisition time. Recently, deep
learning (DL) methods have provided accelerated high-quality image reconstructions from …

Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of
artificial intelligence, which offers many exciting possibilities for musculoskeletal radiology. A …

Purpose Evaluation of a deep learning approach for the detection of meniscal tears and their
characterization (presence/absence of migrated meniscal fragment). Methods A large …

Purpose To investigate the feasibility of using a deep learning–based approach to detect an
anterior cruciate ligament (ACL) tear within the knee joint at MRI by using arthroscopy as the …