Development and validation of a deep-learning model for scoring of radiographic finger joint destruction in rheumatoid arthritis
Development and validation of a deep-learning model for scoring of radiographic finger joint destruction in rheumatoid arthritis
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DOI：
10.1093/rap/rkz047
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发表时间：
2019-01-01
影响因子：
3.1
通讯作者：
Kumanogoh, Atsushi
中科院分区：
文献类型：
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作者：
Hirano, Toru;Nishide, Masayuki;Kumanogoh, Atsushi
目的建立评价放射治疗中手指关节损伤的深度学习模型。方法该模型分为联合检测和联合评价两个步骤。在108名RA患者的216张X线片中，186张X线片被分配到训练/验证数据集，30张被分配到测试数据集。在训练/验证数据集中，临床医生手动剪裁PIP关节、拇指IP关节或MCP关节的图像，并对关节间隙变窄(JSN)和骨侵蚀进行评分，然后对这些图像进行增强。结果，使用11160幅图像来训练和验证用于联合评估的深度卷积神经网络。选择3,720幅图像用于训练机器学习以进行联合检测。这些步骤被结合起来，作为放射学手指关节破坏的评估模型。结果模型对PIP关节、拇指IP关节和MCP关节的检测灵敏度为95.3%，JSN和侵蚀评分为95.3%。准确率(准确符合率)：JSN为49.3~65.4%，侵蚀为70.6~74.1%。模型得分与临床医生每幅图像的相关系数分别为0.72~0.88(JSN)和0.54~0.75(侵蚀)。结论用训练好的卷积神经网络模型进行图像处理是一种可行的方法。
Objective The purpose of this research was to develop a deep-learning model to assess radiographic finger joint destruction in RA.Methods The model comprises two steps: a joint-detection step and a joint-evaluation step. Among 216 radiographs of 108 patients with RA, 186 radiographs were assigned to the training/validation dataset and 30 to the test dataset. In the training/validation dataset, images of PIP joints, the IP joint of the thumb or MCP joints were manually clipped and scored for joint space narrowing (JSN) and bone erosion by clinicians, and then these images were augmented. As a result, 11 160 images were used to train and validate a deep convolutional neural network for joint evaluation. Three thousand seven hundred and twenty selected images were used to train machine learning for joint detection. These steps were combined as the assessment model for radiographic finger joint destruction. Performance of the model was examined using the test dataset, which was not included in the training/validation process, by comparing the scores assigned by the model and clinicians.Results The model detected PIP joints, the IP joint of the thumb and MCP joints with a sensitivity of 95.3% and assigned scores for JSN and erosion. Accuracy (percentage of exact agreement) reached 49.3-65.4% for JSN and 70.6-74.1% for erosion. The correlation coefficient between scores by the model and clinicians per image was 0.72-0.88 for JSN and 0.54-0.75 for erosion.Conclusion Image processing with the trained convolutional neural network model is promising to assess radiographs in RA.