Early detection of radiographic knee osteoarthritis using computer-aided analysis
Received 8 December 2008; accepted 12 April 2009. published online 18 May 2009.
Summary
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.
†Image Informatics and Computational Biology Unit, Laboratory of Genetics, NIA, NIH, 251 Bayview Boulevard, Baltimore, MD 21224, USA
‡Clinical Research Branch, NIA, NIH, 3001 Hanover Street, MD 21225, USA
§Department of Radiology, Johns Hopkins School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA
║Department of Medicine, University of Maryland Medical Center, 22 S. Greene Street, Baltimore, MD 21201, USA
Address correspondence and reprint requests to: L. Shamir, Image Informatics and Computational Biology Unit, Laboratory of Genetics, NIA, NIH, 251 Bayview Boulevard, Baltimore, MD 21224, USA. Tel: 1-410-558-8682; Fax: 1-410-558-8331.
ABSTRACT