Background: Artificial intelligence is gaining traction in automated medical imaging analysis.
Development of more accurate magnetic resonance imaging (MRI) predictors of successful …

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 …

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

Background: Identifying pain generators in multilevel lumbar degenerative disc disease is
not trivial but is crucial for lasting symptom relief with the targeted endoscopic spinal …

Objectives To evaluate a deep learning model for automated and interpretable classification
of central canal stenosis, neural foraminal stenosis, and facet arthropathy from lumbar spine …

Study Design: Narrative review. Objectives: We aim to describe current progress in the
application of artificial intelligence and machine learning technology to provide automated …

Background Artificial intelligence is a revolutionary technology that promises to assist
clinicians in improving patient care. In radiology, deep learning (DL) is widely used in …

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 …

Our objective was to evaluate the diagnostic performance of a convolutional neural network
(CNN) trained on multiple MR imaging features of the lumbar spine, to detect a variety of …

Methods A systematic literature search was conducted in the PubMed and Web of Science
databases using the keywords “deep learning” and “spine”. Date ranges used to conduct the …