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. 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.