医学
膝关节痛
骨关节炎
磁共振成像
神经组阅片室
矢状面
放射科
沃马克
深度学习
膝关节
卷积神经网络
物理疗法
物理医学与康复
人工智能
外科
计算机科学
神经学
病理
替代医学
精神科
作者
Gary H. Chang,David T. Felson,Shangran Qiu,Ali Guermazi,Terence D. Capellini,Vijaya B. Kolachalama
出处
期刊:European Radiology
[Springer Science+Business Media]
日期:2020-02-13
卷期号:30 (6): 3538-3548
被引量:48
标识
DOI:10.1007/s00330-020-06658-3
摘要
It remains difficult to characterize the source of pain in knee joints either using radiographs or magnetic resonance imaging (MRI). We sought to determine if advanced machine learning methods such as deep neural networks could distinguish knees with pain from those without it and identify the structural features that are associated with knee pain.We constructed a convolutional Siamese network to associate MRI scans obtained on subjects from the Osteoarthritis Initiative (OAI) with frequent unilateral knee pain comparing the knee with frequent pain to the contralateral knee without pain. The Siamese network architecture enabled pairwise learning of information from two-dimensional (2D) sagittal intermediate-weighted turbo spin echo slices obtained from similar locations on both knees. Class activation mapping (CAM) was utilized to create saliency maps, which highlighted the regions most associated with knee pain. The MRI scans and the CAMs of each subject were reviewed by an expert radiologist to identify the presence of abnormalities within the model-predicted regions of high association.Using 10-fold cross-validation, our model achieved an area under curve (AUC) value of 0.808. When individuals whose knee WOMAC pain scores were not discordant were excluded, model performance increased to 0.853. The radiologist review revealed that about 86% of the cases that were predicted correctly had effusion-synovitis within the regions that were most associated with pain.This study demonstrates a proof of principle that deep learning can be applied to assess knee pain from MRI scans.• Our article is the first to leverage a deep learning framework to associate MR images of the knee with knee pain. • We developed a convolutional Siamese network that had the ability to fuse information from multiple two-dimensional (2D) MRI slices from the knee with pain and the contralateral knee of the same individual without pain to predict unilateral knee pain. • Our model achieved an area under curve (AUC) value of 0.808. When individuals who had WOMAC pain scores that were not discordant for knees (pain discordance < 3) were excluded, model performance increased to 0.853.
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