Early detection of radiographic knee osteoarthritis using computer-aided analysis

被引:80
作者
Shamir, L. [1 ]
Ling, S. M. [2 ]
Scott, W. [3 ]
Hochberg, M. [4 ]
Ferrucci, L. [2 ]
Goldberg, I. G. [1 ]
机构
[1] NIA, Image Informat & Computat Biol Unit, Genet Lab, NIH, Baltimore, MD 21224 USA
[2] NIA, Clin Res Branch, NIH, Baltimore, MD 21225 USA
[3] Johns Hopkins Sch Med, Dept Radiol, Baltimore, MD 21287 USA
[4] Univ Maryland, Med Ctr, Dept Med, Baltimore, MD 21201 USA
关键词
Image analysis; Osteoarthritis detection; Early detection; ARTICULAR-CARTILAGE; BONE; SELECTION; SEVERITY; TEXTURE; HIP;
D O I
10.1016/j.joca.2009.04.010
中图分类号
R826.8 [整形外科学]; R782.2 [口腔颌面部整形外科学]; R726.2 [小儿整形外科学]; R62 [整形外科学(修复外科学)];
学科分类号
摘要
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. Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International.
引用
收藏
页码:1307 / 1312
页数:6
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