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 …

Objective The purpose of this systematic review was to summarize the results of original
research studies evaluating the characteristics and performance of deep learning models for …

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 interest of researchers, clinicians and radiologists, in artificial intelligence (AI) continues
to grow. Deep learning is a subset of machine learning, in which the computer algorithm …

Radiomics and machine learning–based methods offer exciting opportunities for improving
diagnostic performance and efficiency in musculoskeletal radiology for various tasks …

Deep learning is one of the most exciting new areas in medical imaging. This review article
provides a summary of the current clinical applications of deep learning for lesion detection …

The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders
require radiology imaging (using computed tomography, magnetic resonance imaging …

Deep learning with convolutional neural networks (CNN) is a rapidly advancing subset of
artificial intelligence that is ideally suited to solving image-based problems. There are an …

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 …

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 …