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Published on: July 6, 2022
Early detection of radiographic knee osteoarthritis using computer-aided analysis
1Image Informatics and Computational Biology Unit, Laboratory of Genetics, NIA, NIH, 251 Bayview Boulevard, Baltimore, MD 21224, USA. shamirl@mail.nih.gov
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.
