Enhanced diagnosis of axial spondyloarthritis using machine learning with sacroiliac joint MRI: a multicenter study.

ABSTRACT:

Objectives

To develop a machine learning (ML)-based model using MRI and clinical risk factors to enhance diagnostic accuracy for axial spondyloarthritis (axSpA).

Methods

We retrospectively analyzed datasets from four centers (A-D), focusing on patients with chronic low back pain. A subset from center A was used for prospective validation. A deep learning (DL) model based on ResNet50 was constructed using sacroiliac joint MRI. Clinical variables were integrated with DL scores in ML algorithms to distinguish axSpA from non-axSpA patients. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.

Results

The study included 1294 patients (median age 31 years [interquartile range 24-42]; 35.5

SUBMITTER: Xie Z 

PROVIDER: S-EPMC12031678 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature