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May 8, 2026Journal of Orthopaedic ReportsOpen Access

Multi-Modal Machine Learning Framework with Spatial Alignment of Quantitative MRI and Molecular Priors for Predicting Early Cartilage Degeneration in Osteoarthritis

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Authors

JRJ. RajaPKPravin R. KshirsagarKVK. Vijayan

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Overview

Randomized trial demonstrates improved prediction of early cartilage degeneration in knee osteoarthritis, suggesting enhanced assessment methods.

Key Points

  • To develop a machine learning framework that integrates qMRI biomarkers and molecular data for predicting cartilage degeneration in osteoarthritis.
  • Utilized retrospective datasets from 620 subjects with early-stage knee osteoarthritis.
  • Incorporated molecular pathway signatures from transcriptomic datasets as population-level priors.
  • Trained a gradient boosting model for classification and longitudinal prediction with external validation on 430 subjects.
  • Achieved classification accuracy of 86.8% (95% CI: 83.2–90.1) for early degenerative cartilage regions.
  • Longitudinal prediction model achieved AUC of 0.88 (95% CI: 0.84–0.92).
  • Incorporation of molecular priors significantly enhanced predictive performance compared to imaging-only models.

Cite This Study

Raja et al. (2026) studied this question.

synapsesocial.com/papers/69fd7ddcbfa21ec5bbf06193https://doi.org/10.1016/j.jorep.2026.101044
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