July 29, 2025

Learning From Multiple Readings for Axial Spondyloarthritis Classification of the Sacroiliac Joints

Key Points

This research enhances axial spondyloarthritis classification through automated detection of MRI lesions.
The method targets five MRI lesion types in sacroiliac joints, improving diagnostic consistency.
Validation of the automated approach was conducted on an independent dataset, confirming its reliability.
Automating the classification process addresses issues of intra- and inter-rater variability in MRI assessments.

Abstract

Abstract Magnetic resonance imaging (MRI) is a cornerstone in the evaluation and monitoring of axial spondyloarthritis (axSpA), a chronic inflammatory condition primarily affecting the sacroiliac joints (SIJs), spine, entheses, and peripheral joints. Accurate quantification of axSpA-related changes in MRI is critical for effective research and patient management. However, current lesion detection and grading assessments suffer from substantial intra- and inter-rater variability, limiting their consistency and reliability. This study addresses these challenges by focusing on automated lesion detection in SIJ MRI to enhance accuracy and reduce variability. Our key contributions include: (i) developing a fully automated pipeline for detecting five distinct MRI lesion types (Bone Marrow Oedema, Ankylosis, Sclerosis, Erosions, Fatty Lesions) in the SIJ, (ii) validating the approach on a completely independent dataset, and (iii) proposing a simple approach to learn a classification model from multiple readings or labels for a given sample.

Mark Helpful

Bookmark

Relay