Automated diagnosis and grading of lumbar intervertebral disc degeneration based on a modified YOLO framework.

ABSTRACT:

Background

The high prevalence of low back pain has led to an increasing demand for the analysis of lumbar magnetic resonance (MR) images. This study aimed to develop and evaluate a deep-learning-assisted automated system for diagnosing and grading lumbar intervertebral disc degeneration based on lumbar T2-weighted sagittal and axial MR images.

Methods

This study included a total of 472 patients who underwent lumbar MR scans between January 2021 and November 2023, with 420 in the internal dataset and 52 in the external dataset. The MR images were evaluated and labeled by experts according to current guidelines, and the results were considered the ground truth. The annotations included the Pfirrmann grading of disc degeneration, disc herniation, and high-intensity zones (HIZ)

SUBMITTER: Wang A 

PROVIDER: S-EPMC11794261 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature