Artificial intelligence in axial spondyloarthritis imaging: a review of current evidence

The diagnosis of axial spondyloarthritis (axSpA) remains challenging and is frequently delayed. Imaging is central to diagnosis, but relevant inflammatory and structural abnormalities may be subtle, heterogeneous, or overlap with findings related to mechanical stress, anatomical variation, and degeneration. Artificial intelligence (AI), particularly deep learning, is therefore being investigated to support image interpretation, lesion detection, quantification, and standardized assessment. We synthesize evidence from 12 studies applying convolutional neural networks and related architectures to magnetic resonance imaging and radiographs, automate imaging scores, and combine imaging-derived information with clinical variables. Several models achieved performance comparable to experienced human readers, including during external validation. However, performance may decline in heterogeneous real-world populations for study-specific reasons not explained solely by scanner or protocol differences. No AI tool currently has regulatory clearance for autonomous use in axSpA, and applications remain largely confined to research and clinical trials.