Knee-xRAI: An Explainable AI Framework for Automatic Kellgren-Lawrence Grading of Knee Osteoarthritis

📰 ArXiv cs.AI

Learn how Knee-xRAI, an explainable AI framework, automates Kellgren-Lawrence grading of knee osteoarthritis using modular feature quantification, improving upon current deep learning approaches.

advanced Published 28 Apr 2026
Action Steps
  1. Implement Knee-xRAI using a deep learning library like PyTorch or TensorFlow to quantify joint space narrowing, osteophytes, and subchondral sclerosis.
  2. Train the model on a dataset of radiographic images with corresponding KL grades to improve accuracy.
  3. Evaluate the performance of Knee-xRAI using metrics such as precision, recall, and F1-score.
  4. Compare the results of Knee-xRAI with traditional deep learning approaches to assess its explainability and transparency.
  5. Integrate Knee-xRAI into clinical workflows to support radiologists and orthopedic specialists in diagnosing knee osteoarthritis.
Who Needs to Know This

Radiologists, orthopedic specialists, and AI researchers can benefit from this framework, as it provides a transparent and explainable approach to diagnosing knee osteoarthritis.

Key Insight

💡 Knee-xRAI provides a modular and transparent approach to diagnosing knee osteoarthritis, improving upon current deep learning approaches by quantifying specific radiographic features.

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🚀 Knee-xRAI: An explainable AI framework for automatic Kellgren-Lawrence grading of knee osteoarthritis 📸💡

Key Takeaways

Learn how Knee-xRAI, an explainable AI framework, automates Kellgren-Lawrence grading of knee osteoarthritis using modular feature quantification, improving upon current deep learning approaches.

Full Article

Title: Knee-xRAI: An Explainable AI Framework for Automatic Kellgren-Lawrence Grading of Knee Osteoarthritis

Abstract:
arXiv:2604.23435v1 Announce Type: cross Abstract: Radiographic grading of knee osteoarthritis (KOA) with the Kellgren-Lawrence (KL) system is limited by inter-reader variability and the opacity of current deep learning approaches, which predict KL grades directly from images without decomposing structural features. We present Knee-xRAI, a modular framework that independently quantifies the three cardinal radiographic features of KOA (joint space narrowing [JSN], osteophytes, and subchondral scle
Read full paper → ← Back to Reads

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