Deep learning algorithm for semiquantification of spinal inflammation in axial spondyloarthritis.

ABSTRACT:

Objective

To develop a deep learning algorithm for semiquantification of spinal inflammation in patients with axial spondyloarthritis (SpA).

Methods

The study included 330 participants with axial SpA. All patients underwent whole spine MRI with short τ inversion recovery (STIR) sequence by 3T MR unit. Three independent readers identified regions of interest to locate bone marrow oedema (BMO) and performed Spondyloarthritis Research Consortium of Canada (SPARCC) scoring. Two deep learning models based on attention Unet were developed. The BMO model differentiated image with or without spinal inflammation. The vertebral body (VB)-intervertebral disc (IVD) model identified discovertebral units for localisation. The intraclass correlation coefficient (ICC) and Pearson coefficien

SUBMITTER: Lin Y 

PROVIDER: S-EPMC12815150 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature