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 …

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 MRI is the most commonly used imaging method for diagnosing anterior
cruciate ligament (ACL) injuries. However, the interpretation of knee MRI is time‐intensive …

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 …

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

Objectives To develop a deep-learning algorithm for anterior cruciate ligament (ACL) tear
detection and to compare its accuracy using two external datasets. Methods A database of …

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 …

Deep learning for MRI detection of sports injuries poses unique challenges. To address
these difficulties, this study examines the feasibility and incremental benefit of several …

Anterior cruciate ligament (ACL) tear is caused by partially or completely torn ACL ligament
in the knee, especially in sportsmen. There is a need to classify the ACL tear before it fully …