Early detection of radiographic knee osteoarthritis using computer-aided analysis.
OBJECTIVE
To determine whether computer-based analysis can detect features predictive of osteoarthritis (OA) development in radiographically normal knees.


METHODS
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.


CONCLUSIONS
Radiographic features detectable using a computer-aided image analysis method can predict the future development of radiographic knee OA.
Shamir
L
L
Image Informatics and Computational Biology Unit, Laboratory of Genetics, NIA, NIH, 251 Bayview Boulevard, Baltimore, MD 21224, USA. shamirl@mail.nih.gov
Ling
S M
SM
Scott
W
W
Hochberg
M
M
Ferrucci
L
L
Goldberg
I G
IG
D000465:Algorithms; D042241:Early Diagnosis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007719:Knee Joint / Q000000981:diagnostic imaging*; D008875:Middle Aged; D020370:Osteoarthritis, Knee / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011859:Radiography; D016474:Weight-Bearing