Assessment of osteoarthritis initiativekellgren and lawrence scoring projects quality using computer analysis

Lior Shamir, David T. Felson, Luigi Ferrucci, Ilya G. Goldberg

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


The detection of knee osteoarthritis (OA) is a subjective task, and even two highly experienced and well-trained readers might not always agree on a specific case. This problem is noticeable in OA population studies, in which different scoring projects provide significantly different scores for the same knee X-rays. Here we propose a method for quantitative assessment and comparison of knee X-ray scoring projects in OA population studies. The method works by applying an image analysis method that automatically detects OA in knee X-ray images, and comparing the consistency of the scores when using each of the scoring projects as "gold standard." The method was applied to compare the osteoarthritis initiative (OAI) clinic reading derived Kellgren and Lawrence (K&L) scores to central reading, and showed that when using the derived K&L scores the automatic image analysis method was able to accurately differentiate between healthy joints and moderate OA joints in ∼70% of the cases. When the OAI central reading scores were used as gold standard, the detection accuracy was elevated to ∼77%. These results show that the OAI central readings scores are more consistent with the X-rays, indicating that the central reading better reflects the radiographic features associated with OA, compared to the OAI K&L scores derived from clinic readings.

Original languageEnglish (US)
Pages (from-to)197-201
Number of pages5
JournalJournal of Musculoskeletal Research
Issue number4
StatePublished - Dec 2010
Externally publishedYes


  • Image analysis
  • OA datasets
  • OAI
  • Osteoarthritis Initiative
  • Population studies

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine


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