Retrospective Case-control Study for the Development of an Artificial Intelligence (AI)-Based Tool of Lesion Detection Based on Magnetic Resonance Imaging (MRI) and Clinical Variables for Early Diagnosis of Axial Spondyloarthritis (axSpA)

BACKGROUND

The spondyloarthritis (SpA) are a group of chronic inflammatory diseases of autoimmune nature that share common clinical and genetic features, including an association with HLA-B27 antigen. They are among the most common rheumatic diseases with a prevalence of 0.01-2,5%. All of these conditions make the patients to move on a chronic disabling disease.

Patients with SpA can be classified based on their clinical presentation into either predominantly axial SpA (axSpA) or predominantly peripheral SpA. Axial SpA is characterized by primary involvement of the sacroiliac joints (SIJs) and/or the spine, leaading to substantial pain and disability. Until recently, the diagnosis of axSpA relied on detecting of structural changes evocative of sacroiliitis in the SIJs using plain radiography.

The introduction of Magnetic resonance imaging (MRI) for evaluating the SIJs has significantly advanced the recognition of axSpA. MRI can detect early inflammatory processes even in patients who do not yet have structural lesions. Besides, MRI has shown superiority over radiography in detecting structural changes in the SIJs. However, the definition of a "positive MRI" in SpA remains controversial, as both sensitivity and specificity have their limitations. Early diagnosis of SpA has become increasingly important, as treatments are now available, and MRI is emerging as the preferred choice for early diagnosis. A number of randomized controlled trials of anti-tumour necrosis factor agents in ankylosing spondylitis have demonstrated regression of inflammatory lesions in the spine by MRI. Moreover, the role of MRI in the early diagnosis of SpA has become better established, and imaging features of active sacroiliitis by MRI have been defined for axSpA diagnosis.

RATIONALE OF THE STUDY