Objectives The aim of this study was to determine the feasibility and performance of a deep
learning system used to create synthetic artificial intelligence‐based fat-suppressed …

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 …

Purpose To test the hypothesis that synthetic MRI of the knee generates accurate and
repeatable quantitative maps and produces morphologic MR images with similar quality and …

Objectives The aim of this study was to compare the detection rate of and reader confidence
in 0.55 T knee magnetic resonance imaging (MRI) findings with 3 T knee MRI in patients …

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: This study aimed to evaluate whether the image quality of 1.5 T magnetic
resonance imaging (MRI) of the knee is equal to or higher than that of 3T MRI by applying …

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

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 …

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 …