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May 3, 2026SHILAP Revista de lepidopterologíaOpen Access

Artificial intelligence in Kellgren–Lawrence grading of knee osteoarthritis: bridging radiographic tradition with algorithmic precision

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Authors

SRSaumya RawatVCVed ChaturvediBVBinit Vaidya

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Overview

Narrative review reveals AI enhances Kellgren-Lawrence grading accuracy for knee osteoarthritis, suggesting clinical potential.

Key Points

  • The aim is to explore advancements in artificial intelligence for enhancing Kellgren-Lawrence grading of knee osteoarthritis on radiographs.
  • Narrative review of peer-reviewed studies from 2016 to 2025 on AI methods for KL grading.
  • Data sources included PubMed, Embase, Web of Science, and Google Scholar.
  • Eighteen studies considered, focusing on model architectures and performance metrics.
  • Automated KL grading achieved accuracies between 75% and 98%, with AUC values up to 0.98.
  • Agreement with expert assessments indicated a Cohen's kappa (κ) ranging from 0.67 to 0.86.
  • Challenges noted include subjective labeling, dataset imbalance, and limited external validation.

Cite This Study

Rawat et al. (2026) studied this question.

synapsesocial.com/papers/69f6e5868071d4f1bdfc63b2https://doi.org/10.1177/1759720x261442408
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