Background: Artificial intelligence could provide more accurate magnetic resonance imaging
(MRI) predictors of successful clinical outcomes in targeted spine care. Objective: To …

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

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 …

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

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 …

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 …

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 …

Purpose To evaluate the ability of an artificial intelligence (AI) tool in magnetic resonance
imaging (MRI) assessment of degenerative pathologies of lumbar spine using radiologist …

Study design Investigation of the automation of radiological features from magnetic
resonance images (MRIs) of the lumbar spine. Objective To automate the process of grading …

OBJECTIVE Magnetic resonance imaging (MRI) is the gold-standard tool for diagnosing
lumbar spinal stenosis (LSS), but it is difficult to promptly examine all suspected cases with …