PDF(Pubmed)
Abstract：
OBJECTIVE: Rheumatoid arthritis (RA) is a severe and common autoimmune disease. Conventional diagnostic methods are often subjective, error-prone, and repetitive works. There is an urgent need for a method to detect RA accurately. Therefore, this study aims to develop an automatic diagnostic system based on deep learning for recognizing and staging RA from radiographs to assist physicians in diagnosing RA quickly and accurately.
METHODS: We develop a CNN-based fully automated RA diagnostic model, exploring five popular CNN architectures on two clinical applications. The model is trained on a radiograph dataset containing 240 hand radiographs, of which 39 are normal and 201 are RA with five stages. For evaluation, we use 104 hand radiographs, of which 13 are normal and 91 RA with five stages.
RESULTS: The CNN model achieves good performance in RA diagnosis based on hand radiographs. For the RA recognition, all models achieve an AUC above 90% with a sensitivity over 98%. In particular, the AUC of the GoogLeNet-based model is 97.80%, and the sensitivity is 100.0%. For the RA staging, all models achieve over 77% AUC with a sensitivity over 80%. Specifically, the VGG16-based model achieves 83.36% AUC with 92.67% sensitivity.
CONCLUSIONS: The presented GoogLeNet-based model and VGG16-based model have the best AUC and sensitivity for RA recognition and staging, respectively. The experimental results demonstrate the feasibility and applicability of CNN in radiograph-based RA diagnosis. Therefore, this model has important clinical significance, especially for resource-limited areas and inexperienced physicians.
METHODS: We develop a CNN-based fully automated RA diagnostic model, exploring five popular CNN architectures on two clinical applications. The model is trained on a radiograph dataset containing 240 hand radiographs, of which 39 are normal and 201 are RA with five stages. For evaluation, we use 104 hand radiographs, of which 13 are normal and 91 RA with five stages.
RESULTS: The CNN model achieves good performance in RA diagnosis based on hand radiographs. For the RA recognition, all models achieve an AUC above 90% with a sensitivity over 98%. In particular, the AUC of the GoogLeNet-based model is 97.80%, and the sensitivity is 100.0%. For the RA staging, all models achieve over 77% AUC with a sensitivity over 80%. Specifically, the VGG16-based model achieves 83.36% AUC with 92.67% sensitivity.
CONCLUSIONS: The presented GoogLeNet-based model and VGG16-based model have the best AUC and sensitivity for RA recognition and staging, respectively. The experimental results demonstrate the feasibility and applicability of CNN in radiograph-based RA diagnosis. Therefore, this model has important clinical significance, especially for resource-limited areas and inexperienced physicians.
摘要：
目的：类风湿性关节炎（RA）是一种严重且常见的自身免疫性疾病。传统的诊断方法往往是主观的，容易出错，重复的工作。迫切需要一种准确检测RA的方法。因此,本研究旨在开发一种基于深度学习的自动诊断系统,用于从X光片对RA进行识别和分期,以帮助医师快速准确地诊断RA.
方法：我们开发了基于CNN的全自动RA诊断模型，在两个临床应用中探索五种流行的CNN架构。该模型是在包含240张手射线照片的射线照片数据集上训练的，其中39是正常的，201是RA，有五个阶段。为了评估，我们用了104张手部射线照片,其中13个是正常的，91个RA有五个阶段。
结果：CNN模型在基于手部射线照片的RA诊断中实现了良好的性能。对于RA识别，所有模型的AUC均超过90%，灵敏度超过98%。特别是,基于GoogLeNet的模型的AUC为97.80%，灵敏度为100.0%。对于RA分期，所有模型的AUC均超过77%，灵敏度超过80%。具体来说,基于VGG16的模型具有83.36%的AUC和92.67%的灵敏度。
结论：提出的基于GoogLeNet的模型和基于VGG16的模型对RA识别和分期具有最佳的AUC和灵敏度，分别。实验结果证明了CNN在基于X射线的RA诊断中的可行性和适用性。因此,该模型具有重要的临床意义,特别是对于资源有限的地区和缺乏经验的医生。
方法：我们开发了基于CNN的全自动RA诊断模型，在两个临床应用中探索五种流行的CNN架构。该模型是在包含240张手射线照片的射线照片数据集上训练的，其中39是正常的，201是RA，有五个阶段。为了评估，我们用了104张手部射线照片,其中13个是正常的，91个RA有五个阶段。
结果：CNN模型在基于手部射线照片的RA诊断中实现了良好的性能。对于RA识别，所有模型的AUC均超过90%，灵敏度超过98%。特别是,基于GoogLeNet的模型的AUC为97.80%，灵敏度为100.0%。对于RA分期，所有模型的AUC均超过77%，灵敏度超过80%。具体来说,基于VGG16的模型具有83.36%的AUC和92.67%的灵敏度。
结论：提出的基于GoogLeNet的模型和基于VGG16的模型对RA识别和分期具有最佳的AUC和灵敏度，分别。实验结果证明了CNN在基于X射线的RA诊断中的可行性和适用性。因此,该模型具有重要的临床意义,特别是对于资源有限的地区和缺乏经验的医生。
