Predicting the mechanical hip-knee-ankle angle accurately from standard knee radiographs: a cross-validation experiment in 100 patients.
Predicting the mechanical hip-knee-ankle angle accurately from standard knee radiographs: a cross-validation experiment in 100 patients.
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DOI：
10.1080/17453674.2020.1779516
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发表时间：
2020-12
影响因子：
3.7
通讯作者：
Custers RJH
中科院分区：
文献类型：
--
作者：
Gielis WP;Rayegan H;Arbabi V;Ahmadi Brooghani SY;Lindner C;Cootes TF;de Jong PA;Weinans H;Custers RJH
背景和目的-能够从标准的膝关节X线片预测髋-膝-踝角（HKAA），使得对缺乏全肢X线片的队列中的对线不良进行研究成为可能。我们的目标是开发一个自动化的图像分析管道，以测量标准膝关节X线片的股骨-胫骨角（FTA），并测试各种FTA定义，以预测HKAA。患者和方法-我们包括110对标准的膝关节和全肢X线片。自动搜索算法在标准膝关节X光片上找到解剖标志。基于这些标志，FTA根据9种不同的定义（6种在文献中描述，3种新开发）自动计算。Pearson和类内相关系数[ICC]）确定FTA和HKAA之间的测量全肢X线片。随后，前4个FTA定义被用来预测HKAA在5倍交叉验证设置。结果-在所有图像对中，FTA和HKAA之间的Pearson相关性在0.83和0.90之间。ICC值为0.83至0.90。在预测HKAA的交叉验证实验中，这些值仅略有下降。根据标准膝关节X线片预测HKAA的最佳方法的平均绝对误差为1.8°（SD 1.3）。解释-我们表明，HKAA可以自动预测标准膝关节X线片与公平的准确性和高相关性相比，真正的HKAA。因此，该方法能够在缺乏全肢X线摄影的大型（流行病学）研究中研究对线不良与膝关节病理之间的关系。
Background and purpose — Being able to predict the hip–knee–ankle angle (HKAA) from standard knee radiographs allows studies on malalignment in cohorts lacking full-limb radiography. We aimed to develop an automated image analysis pipeline to measure the femoro-tibial angle (FTA) from standard knee radiographs and test various FTA definitions to predict the HKAA. Patients and methods — We included 110 pairs of standard knee and full-limb radiographs. Automatic search algorithms found anatomic landmarks on standard knee radiographs. Based on these landmarks, the FTA was automatically calculated according to 9 different definitions (6 described in the literature and 3 newly developed). Pearson and intra-class correlation coefficient [ICC]) were determined between the FTA and HKAA as measured on full-limb radiographs. Subsequently, the top 4 FTA definitions were used to predict the HKAA in a 5-fold cross-validation setting. Results — Across all pairs of images, the Pearson correlations between FTA and HKAA ranged between 0.83 and 0.90. The ICC values from 0.83 to 0.90. In the cross-validation experiments to predict the HKAA, these values decreased only minimally. The mean absolute error for the best method to predict the HKAA from standard knee radiographs was 1.8° (SD 1.3). Interpretation — We showed that the HKAA can be automatically predicted from standard knee radiographs with fair accuracy and high correlation compared with the true HKAA. Therefore, this method enables research of the relationship between malalignment and knee pathology in large (epidemiological) studies lacking full-limb radiography.
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影响因子：
7
作者：
Gielis, W. P.;Weinans, H.;Lindner, C.
通讯作者：
Lindner, C.
DOI：
10.1002/art.22618
发表时间：
2007-04-15
期刊：
ARTHRITIS & RHEUMATISM-ARTHRITIS CARE & RESEARCH
影响因子：
--
作者：
Issa, Sakeba N.;Dunlop, Dorothy;Sharma, Leena
通讯作者：
Sharma, Leena
影响因子：
--
作者：
Waarsing, J. H.;Kloppenburg, M.;Meulenbelt, I.
通讯作者：
Meulenbelt, I.
影响因子：
7
作者：
Sheehy, L.;Felson, D.;Zhang, Y.;Niu, J.;Lam, Y. -M.;Segal, N.;Lynch, J.;Cooke, T. D. V.
通讯作者：
Cooke, T. D. V.
影响因子：
--
作者：
Kraus, VB;Vail, TP;McDaniel, G
通讯作者：
McDaniel, G