Spinal magnetic resonance (MR) scans are a vital tool for diagnosing the cause of back pain
for many diseases and conditions. However, interpreting clinically useful information from …

This paper proposes a novel transformer-based model architecture for medical imaging
problems involving analysis of vertebrae. It considers two applications of such models in MR …

Accurate fine-grained grading of lumbar intervertebral disc (LIVD) degeneration is essential
for the diagnosis and treatment design of high-incidence low back pain. However, the …

We propose a general pipeline to automate the extraction of labels from radiology reports
using large language models, which we validate on spinal MRI reports. The efficacy of our …

Study Design. This is a retrospective observational study to externally validate a deep
learning image classification model. Objective. Deep learning models such as SpineNet …

Objective Modic changes (MCs) are the most prevalent classification system for describing
intravertebral MRI signal intensity changes. However, interpreting these intricate MRI …

This paper addresses the challenge of grading visual features in lumbar spine MRI using
Deep Learning. Such a method is essential for the automatic quantification of structural …

ABSTRACT Objective Modic changes (MCs) classification system is the most widely used
method in magnetic resonance imaging (MRI) for characterizing subchondral vertebral …

Background In recent years, the integration of Artificial Intelligence (AI) models has
revolutionized the diagnosis of Low Back Pain (LBP) and associated disc pathologies …

The increasing prevalence of lumbar spinal canal stenosis has resulted in a surge of MRI
imaging, leading to laborintensive interpretation and significant inter-reader variability, even …