AI-based lumbar central canal stenosis classification on sagittal MR images is comparable to experienced radiologists using axial images.

ABSTRACT:

Objectives

The assessment of lumbar central canal stenosis (LCCS) is crucial for diagnosing and planning treatment for patients with low back pain and neurogenic pain. However, manual assessment methods are time-consuming, variable, and require axial MRIs. The aim of this study is to develop and validate an AI-based model that automatically classifies LCCS using sagittal T2-weighted MRIs.

Methods

A pre-existing 3D AI algorithm was utilized to segment the spinal canal and intervertebral discs (IVDs), enabling quantitative measurements at each IVD level. Four musculoskeletal radiologists graded 683 IVD levels from 186 LCCS patients using the 4-class Lee grading system. A second consensus reading was conducted by readers 1 and 2, which, along with automatic measurements, formed

SUBMITTER: van der Graaf JW 

PROVIDER: S-EPMC11913898 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature