ArticleFrontiers in physiology2026
Standard knee radiographs enable deep learning inference of MRI-defined cartilage and meniscal damage in early knee osteoarthritis: a study using the osteoarthritis initiative database.
Article in Frontiers in physiology, 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
Background: To develop and validate a radiograph-only deep learning framework that jointly predicts magnetic resonance imaging (MRI)-defined early structural pathology in knees with early radiographic osteoarthritis (Kellgren-Lawrence [KL] grades 0-1) and performs full-spectrum KL classification across all grades within a single multi-task architecture. Methods: This retrospective study used baseline data from the Osteoarthritis Initiative (OAI) comprising 8260 knees (4130 participants). A multi-task ConvNeXt-Base network was trained with five-fold stratified group cross-validation enforcing strict subject-level data separation. KL classification was supervised across all knees. MRI prediction heads were activated only for KL 0-1 knees with available labels. MRI Osteoarthritis Knee Score (MOAKS)-derived binary endpoints were defined at a threshold of ≥2: tibiofemoral cartilage damage (primary endpoint) and meniscal morphology damage (secondary endpoint). Results: KL grading performance across held-out folds was as follows: mean quadratic weighted kappa (QWK) 0.8284 (standard deviation [SD] 0.0255), balanced accuracy 0.6836 (SD 0.0159). In KL0/1 knees with both MRI labels (n = 2561), prevalence was 31.3% for cartilage damage and 26.4% for meniscal damage. Mean AUROC/AUPRC were 0.7329 (SD 0.0203)/0.5741 (SD 0.0364) for cartilage damage and 0.7193 (SD 0.0533)/0.5147 (SD 0.0719) for meniscal damage. Conclusions: A leakage-controlled multi-task radiograph model achieved strong KL grading agreement and moderate discrimination of MRI-defined cartilage and meniscal pathology in knees with early radiographic osteoarthritis, supporting a potential role as an assistive triage signal to identify patients who may benefit from earlier MRI evaluation. However, clinical correlation remains essential; MRI referral decisions should always be made in the context of the individual patient's symptoms, functional impairment, and overall clinical assessment, and cannot be based on AI-derived probability scores alone.
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