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 …

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 …

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 …

The most commonly injured ligament in the human body is an anterior cruciate ligament
(ACL). ACL injury is standard among the football, basketball and soccer players. The study …

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 …

Objectives The aim of this study was to clinically validate a Deep Convolutional Neural
Network (DCNN) for the detection of surgically proven anterior cruciate ligament (ACL) tears …

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 …

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 …

Purpose To evaluate the diagnostic utility of two convolutional neural networks (CNNs) for
severity staging of anterior cruciate ligament (ACL) injuries. Materials and Methods In this …

Purpose To (1) determine the diagnostic efficacy of artificial intelligence (AI) methods for
detecting anterior cruciate ligament (ACL) and meniscus tears and to (2) compare the …