Knee osteoarthritis severity level classification using whole knee cartilage damage Index and ANN
Knee osteoarthritis severity level classification using whole knee cartilage damage Index and ANN
复制标题
使用全膝软骨损伤指数和人工神经网络对膝骨关节炎严重程度进行分类
DOI：
10.1145/3278576.3278585
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Zhang, Ming
中科院分区：
文献类型：
--
作者：
Du, Yaodong;Shan, Juan;Almajalid, Rania;Zhang, Ming
在这项研究中，我们扩展了我们以前的工作，分析软骨厚度和骨关节炎（OA）严重程度变化之间的关系。通过软骨损伤指数（CDI）测量软骨厚度，该指数包括每个膝关节在3D MRI上标记的60个点。在我们之前的工作中，我们仅使用股骨和胫骨间室上的CDI点（36个点）作为特征，并采用机器学习方法来预测OA严重程度等级变化。在这项工作中，我们将髌骨上的24个CDI点添加到特征空间中，并在更大的数据集上探索髌骨上的CDI点是否可以提高准确性。本研究中使用Kelling-Lawrence（KL）分级来衡量OA严重程度。采用在我们之前的研究中表现出良好性能的人工神经网络（ANN）作为机器学习方法。对于KL等级分类，实验结果表明，增加髌骨点的性能显着提高，从AUC 0.822到AUC 0.903和全膝CDI在数据集上取得了最好的分类性能。
In this study, we extended our previous work on analyzing the relationship between cartilage thickness and osteoarthritis (OA) severity grade change. Cartilage thickness is measured by the Cartilage Damage Index (CDI) which includes 60 points marked on 3D MRI for each knee joint. In our previous work, we used CDI points on femur and tibia compartments only (36 points) as features and employed machine learning methods to predict the OA severity grade change. In this work, we added the 24 CDI points on patella into the feature space and explored whether CDI points from patella could improve the accuracy, on a larger dataset. Kellgren-Lawrence (KL) grade was used to measure OA severity in this study. Artificial neural network (ANN), which showed good performance in our previous study, was employed as the machine learning method. For KL grade classification, experiment results showed that adding patella points improved the performance remarkably, from AUC 0.822 to AUC 0.903 and the whole knee CDI achieved the best classification performance on the dataset.