Purpose To develop a deep learning model to accurately detect anterior cruciate ligament
(ACL) ruptures on magnetic resonance imaging (MRI) and to evaluate its effect on the …

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 …

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 To develop a fully automated CNN detection system based on magnetic
resonance imaging (MRI) for ACL injury, and to explore the feasibility of CNN for ACL injury …

Background The development of computer-assisted technologies to diagnose anterior
cruciate ligament (ACL) injury by analyzing knee magnetic resonance images (MRI) would …

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

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 is an advanced machine learning approach used in diverse areas such as
image analysis, bioinformatics, and natural language processing. In the current study, using …

Background: Although many machine learning algorithms have been developed to detect
anterior cruciate ligament (ACL) injury based on magnetic resonance imaging (MRI), the …