ArticleBMC musculoskeletal disorders2026
Clinically integrated multi-modal transformer framework with cross-modal gated fusion and clinical nomogram for automated Kellgren-Lawrence grading of knee osteoarthritis on x-ray images.
Article in BMC musculoskeletal disorders, 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
backgroundWe developed a multi-modal Transformer framework integrating knee radiographs with clinical covariates to enable automated, objective, and generalizable ordinal Kellgren-Lawrence (KL) grading.
methodsA total of 2,703 anteroposterior knee radiographs were retrospectively collected from three independent medical centers (January 2018 - December 2024). Data from two centers (n = 1,953) were used for model development and internal five-fold stratified cross-validation, while the third center (n = 750) served as an independent external test set. The proposed framework combines a Swin Transformer-Base image encoder with a clinical feature Transformer through a novel Robust Cross-Modal Gated Fusion (RCGF) module employing bidirectional cross-attention and uncertainty-aware dynamic gating via Monte-Carlo dropout. Ordinal prediction was performed using Consistent Rank Logits (CORAL). Eight classifier architectures were systematically compared, encompassing multi-modal models, unimodal image-only baselines, and a clinical-only model.
resultsThe proposed RCGF framework achieved a Quadratic Weighted Kappa (QWK) of 0.900 (95% CI: 0.877-0.921), macro-averaged AUC of 0.930 (95% CI: 0.910-0.950), and balanced accuracy of 87.6% on the independent external test set, significantly outperforming all baseline models including BioViL-T (QWK = 0.850) and MedViT (QWK = 0.830; all FDR-corrected p < 0.001). Sensitivity for severe Osteoarthritis (OA) (Grade 4) reached 83.5% (95% CI: 79.1-87.4%), with specificity 95.3%. The clinical nomogram demonstrated excellent calibration (calibration slope = 0.98, Brier score = 0.072, C-statistic = 0.940) and superior net benefit over treat-all and treat-none strategies across all clinically relevant decision thresholds.
conclusionThis multi-modal Transformer framework with uncertainty-aware gated fusion provides robust external generalizability for ordinal knee OA severity grading and delivers a clinically actionable nomogram. The approach has strong potential to reduce radiologist workload and facilitate objective assessment on routine clinical radiographs, particularly in resource-constrained settings.
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