Imaging pathways in spondyloarthritis: integrating radiography, ultrasonography, magnetic resonance imaging, low-dose computed tomography, and artificial intelligence methods : Radiology and AI in SpA.

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Abstract

Spondyloarthritis (SpA) refers to a family of chronic inflammatory rheumatic conditions characterized by axial and/or peripheral manifestations. Early detection of SpA is crucial for improving long-term patient outcomes and necessitates a refined diagnostic algorithm. This literature review addresses current recommendations for imaging approaches in SpA, proposes a contemporary diagnostic algorithm for suspected axial SpA, and discusses current and emerging applications of artificial intelligence (AI) in diagnosis and management. A comprehensive literature search of PubMed, Embase and Scopus was performed for studies published between January 2010 and August 2025. Relevant English-language studies on imaging modalities and AI applications in SpA were included after independent screening. The implementation of advanced imaging techniques-such as low-dose computed tomography (CT) for detailed structural assessment and standardized magnetic resonance imaging (MRI) protocols for detecting inflammatory changes-has improved the diagnostic evaluation of sacroiliac joints. Incorporating clinical features and modality-specific strengths helps tailor imaging choices to individual patients with suspected SpA. In clinical practice, MRI may be considered for early detection of sacroiliitis-especially in younger patients and those with short symptom duration-whereas conventional radiography continues to serve as the recommended first-line imaging modality in many diagnostic pathways. Low-dose CT should be reserved for selected cases, such as inconclusive MRI findings, contraindications to MRI, limited MRI availability, or a specific need to assess structural damage. Advances in AI, particularly in deep learning, have had a remarkable impact on medical research. Despite existing limitations, such as costs of deployment and medico-legal considerations, their role in rheumatological imaging is being actively investigated. Deep learning-based models trained on radiographic, CT and MRI datasets have demonstrated progressively greater precision in detecting sacroiliitis, becoming a powerful tool that complements human judgement. Prospective strategies integrating multimodal imaging, AI-assisted interpretation, and prognostic assessment may enhance diagnostic accuracy and provide personalized therapeutic solutions in SpA.

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