Early detection of radiographic knee osteoarthritis using computer-aided analysis

L. Shamir, S. M. Ling, W. Scott, M. Hochberg, L. Ferrucci, I. G. Goldberg

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


Objective: To determine whether computer-based analysis can detect features predictive of osteoarthritis (OA) development in radiographically normal knees. Method: A systematic computer-aided image analysis method weighted neighbor distances using a compound hierarchy of algorithms representing morphology (WND-CHARM) was used to analyze pairs of weight-bearing knee X-rays. Initial X-rays were all scored as normal Kellgren-Lawrence (KL) grade 0, and on follow-up approximately 20 years later either developed OA (defined as KL grade = 2) or remained normal. Results: The computer-aided method predicted whether a knee would change from KL grade 0 to grade 3 with 72% accuracy (P < 0.00001), and to grade 2 with 62% accuracy (P < 0.01). Although a large part of the predictive signal comes from the image tiles that contained the joint, the region adjacent to the tibial spines provided the strongest predictive signal. Conclusion: Radiographic features detectable using a computer-aided image analysis method can predict the future development of radiographic knee OA.

Original languageEnglish (US)
Pages (from-to)1307-1312
Number of pages6
JournalOsteoarthritis and Cartilage
Issue number10
StatePublished - Oct 2009
Externally publishedYes


  • Early detection
  • Image analysis
  • Osteoarthritis detection

ASJC Scopus subject areas

  • Rheumatology
  • Biomedical Engineering
  • Orthopedics and Sports Medicine


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