Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network
Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network
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DOI：
10.1007/s10067-019-04487-4
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发表时间：
2020-04-01
影响因子：
3.4
通讯作者：
Maras, Hadi Hakan
中科院分区：
文献类型：
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作者：
Ureten, Kemal;Erbay, Hasan;Maras, Hadi Hakan
手平片是类风湿性关节炎（RA）诊断或鉴别诊断以及监测疾病活动的一线和最常用的成像方法。在这项研究中，我们使用普通的手部X光片，并试图开发一种使用卷积神经网络的自动诊断方法，以帮助医生诊断类风湿性关节炎。卷积神经网络（CNN）是一种基于多层神经网络结构的深度学习方法。该网络在包含135张右手X光片的数据集上进行训练，其中61张正常，74张RA，并在45张X光片上进行测试，其中20张正常，25张RA。结果该网络的准确率为73.33%，误差率为0.0167.该网络的灵敏度为0.6818，特异度为0.7826，精确度为0.7500。结论仅利用手部X光片上的像素信息，设计了一种具有在线数据增强的多层CNN结构。性能指标，如准确性，错误率，灵敏度，特异性和精度状态表明，该网络是有前途的类风湿关节炎的诊断。
Introduction Plain hand radiographs are the first-line and most commonly used imaging methods for diagnosis or differential diagnosis of rheumatoid arthritis (RA) and for monitoring disease activity. In this study, we used plain hand radiographs and tried to develop an automated diagnostic method using the convolutional neural networks to help physicians while diagnosing rheumatoid arthritis. Methods A convolutional neural network (CNN) is a deep learning method based on a multilayer neural network structure. The network was trained on a dataset containing 135 radiographs of the right hands, of which 61 were normal and 74 RA, and tested it on 45 radiographs, of which 20 were normal and 25 RA. Results The accuracy of the network was 73.33% and the error rate 0.0167. The sensitivity of the network was 0.6818; the specificity was 0.7826 and the precision 0.7500. Conclusion Using only pixel information on hand radiographs, a multi-layer CNN architecture with online data augmentation was designed. The performance metrics such as accuracy, error rate, sensitivity, specificity, and precision state shows that the network is promising in diagnosing rheumatoid arthritis.