#4-01E. Seeing Beyond the Surface: AI and the Future of Cartilage Imaging (NatRevRheumatol, 2026)

How early can we confidently detect cartilage degeneration in osteoarthritis and other rheumatic diseases before irreversible structural damage occurs? This comprehensive review details the recent advancements in non-invasive cartilage imaging, exploring the transition from conventional morphological MRI to state-of-the-art compositional techniques—such as T2 mapping, T1ρ, and ultra-short echo-time imaging—that meticulously assess extracellular matrix ultrastructure. The most transformative clinical takeaway highlights how artificial intelligence is currently revolutionizing the field by drastically accelerating image acquisition and enabling automated, quantitative cartilage segmentation, which is pivotal for sensitive early diagnosis. However, when critically appraising this literature, clinicians must remain cautious regarding AI implementation; many deep-learning models currently suffer from a "domain shift" because they rely on outdated training datasets, potentially leading to the under-segmentation of true pathological lesions when applied to contemporary imaging sequences. For practicing rheumatologists and orthopedic physicians, understanding both the immense capabilities of these advanced imaging modalities and the current technical limitations of AI integration is absolutely crucial for optimizing individualized patient management and improving early, targeted therapeutic interventions.

Citation: Guermazi, Ali, et al. Advances in cartilage imaging techniques. Nature Reviews Rheumatology. 2026. DOI: 10.1038/s41584-026-01353-x

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