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

Objectives The aim of this study was to analyze the accuracy and positive predictive value
(PPV) of preoperative lumbar MRI grading for successful outcome after outpatient …

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

Purpose Current findings suggest that minimally percutaneous endoscopic lumbar
discectomy (PELD) is a practical therapeutic approach for lumbar disc herniation (LDH) …

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 …

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 …

Abstract Background Context Percutaneous endoscopic lumbar discectomy (PELD) is one of
minimally invasive techniques to treat patients with low back and radiating pain resulting …

Purpose A common spine surgery procedure involves decompression of the lumbar spine.
The impact of the surgeon's learning curve on relevant clinical outcomes is currently not well …

Abstract Background Context Both Transforaminal (TF) and Interlaminar (IL) endoscopic
approaches are established techniques of decompression for lumbar compressive …