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Related Experiment Videos

Predicting Knee osteoarthritis progression using explainable machine learning and clinical imaging data.

Rayan Harari1,2, Stacy E Smith1,3, Sara M Bahouth1

  • 1Department of Radiology, Mass General Brigham, Harvard Medical School, Boston, MA, USA.

Osteoarthritis and Cartilage Open
|June 29, 2026
PubMed
Summary

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This summary is machine-generated.

Explainable AI accurately predicts knee osteoarthritis radiographic progression using MRI and clinical data. Key factors include cartilage loss and bone marrow lesions, offering insights into disease development.

Area of Science:

  • Orthopedics
  • Radiology
  • Artificial Intelligence

Background:

  • Knee osteoarthritis (KOA) poses a significant health burden.
  • Predicting KOA progression is crucial for timely intervention.
  • Quantitative MRI and clinical data offer rich insights into KOA.

Purpose of the Study:

  • To evaluate explainable AI models for KOA progression prediction.
  • To assess models for both composite (radiographic + pain) and radiographic-only endpoints.
  • To identify key predictive features using explainability methods.

Main Methods:

  • Analysis of 600 participants from the FNIH Osteoarthritis Biomarkers Consortium (OAI).
  • Utilized demographic, clinical, and quantitative MRI features (cartilage, bone marrow lesions, osteophytes, effusion-synovitis).
Keywords:
BiomarkersDisease progressionExplainable AIFNIHKnee osteoarthritisMRIMachine learning

Related Experiment Videos

  • Employed five classifiers (e.g., random forest, XGBoost) with explainability techniques (SHAP, Gini importance).

    • Main Results:

    • Radiographic progression was accurately predicted (AUC=0.87) using longitudinal MRI changes.
    • Baseline features also showed strong predictive performance (AUC=0.80).
    • Composite progression prediction was less accurate (AUCs=0.66-0.70).

    Conclusions:

    • Explainable AI with quantitative MRI enables interpretable KOA progression prediction.
    • This study integrates longitudinal MRI features and model-agnostic explanations in the FNIH/OAI cohort.
    • Identified medial femoral cartilage loss, bone marrow lesions, osteophytes, and effusion-synovitis as key predictors.