ArticleFrontiers in digital health2026
Objective stratification of knee osteoarthritis stages using a semi-supervised learning approach on multimodal MRI-CT cartilage features.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Knee osteoarthritis (KOA) is a chronic and progressive joint disease that affects middle-aged and older adults. Early detection is crucial to prevent progression toward joint replacement and improve long-term outcomes, yet current diagnoses are strongly influenced by subjective symptoms, especially pain perception, which varies widely across individuals and does not reliably reflect structural degeneration. This study introduces a semi-supervised learning (SSL) framework for characterizing KOA stages through combined MRI and CT-derived cartilage features. Methods: A cohort of 133 knee scans was analyzed, including 36 expert-labeled cases categorized as healthy, early degeneration, or advanced degeneration. These labels served as seeds for graph-based SSL using Label Propagation and Label Spreading, producing pseudo-labels for the remaining samples. Results: Label stability across ten Monte Carlo runs demonstrated high agreement (0.91 Discussion: The volume-to-surface ratio and density heterogeneity demonstrated the strongest discriminatory power, reflecting progressive cartilage thinning, surface irregularity, and increasing structural heterogeneity consistent with KOA pathophysiology. These results show that combining expert knowledge with SSL enables reliable KOA stratification even with limited labeled data, offering meaningful insights into cartilage degeneration and laying the foundation for quantitative and more objective imaging-based biomarkers and future continuous scoring systems.
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