The major and upward trend in the number of published research related to rheumatic and
musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the …

Background Assessment of lumbar spinal stenosis at MRI is repetitive and time consuming.
Deep learning (DL) could improve productivity and the consistency of reporting. Purpose To …

Objective Convolutional neural networks (CNNs) are being increasingly used in the medical
field, especially for image recognition in high-resolution, large-volume data sets. The study …

Background Lumbar spine MRI studies are widely used for back pain assessment.
Interpretation involves grading lumbar spinal stenosis, which is repetitive and time …

Background Histopathological analysis of intervertebral disc (IVD) tissues is a critical
domain of back pain research. Identification, description, and classification of attributes that …

Background Magnetic resonance imaging (MRI) is used to detect degenerative changes of
the lumbar spine. SpineNet (SN), a computer vision-based system, performs an automated …

Objectives The aim of this study was to clinically validate a fully automated AI model for
magnetic resonance imaging (MRI)–based quantifications of lumbar spinal canal stenosis …

Background Lumbar spinal stenosis (LSS) is a major cause of pain and disability in older
individuals worldwide. Although increasing studies of traditional machine learning (TML) …

Study Design. The present study followed the reporting guidelines for systematic reviews
and meta-analyses. Objective. Therefore, we conducted this study to review the diagnostic …

Recent artificial intelligence (AI) advances may potentially transform all aspects of radiology
including acquisition, interpretation, and radiology report generation, enhancing accuracy …