Department of Internal Medicine, Division of Rheumatology, Keio University School of Medicine, Tokyo, Japan.
Medical AI Center, Keio University School of Medicine, Tokyo, Japan.
PLoS One. 2023 Feb 13;18(2):e0281088. doi: 10.1371/journal.pone.0281088. eCollection 2023.
We propose a wrist joint subluxation/ankylosis classification model for an automatic radiographic scoring system for X-ray images. In managing rheumatoid arthritis, the evaluation of joint destruction is important. The modified total Sharp score (mTSS), which is conventionally used to evaluate joint destruction of the hands and feet, should ideally be automated because the required time depends on the skill of the evaluator, and there is variability between evaluators. Since joint subluxation and ankylosis are given a large score in mTSS, we aimed to estimate subluxation and ankylosis using a deep neural network as a first step in developing an automatic radiographic scoring system for joint destruction. We randomly extracted 216 hand X-ray images from an electronic medical record system for the learning experiments. These images were acquired from patients who visited the rheumatology department of Keio University Hospital in 2015. Using our newly developed annotation tool, well-trained rheumatologists and radiologists labeled the mTSS to the wrist, metacarpal phalangeal joints, and proximal interphalangeal joints included in the images. We identified 21 X-ray images containing one or more subluxation joints and 42 X-ray images with ankylosis. To predict subluxation/ankylosis, we conducted five-fold cross-validation with deep neural network models: AlexNet, ResNet, DenseNet, and Vision Transformer. The best performance on wrist subluxation/ankylosis classification was as follows: accuracy, precision, recall, F1 value, and AUC were 0.97±0.01/0.89±0.04, 0.92±0.12/0.77±0.15, 0.77±0.16/0.71±0.13, 0.82±0.11/0.72±0.09, and 0.92±0.08/0.85±0.07, respectively. The classification model based on a deep neural network was trained with a relatively small dataset; however, it showed good accuracy. In conclusion, we provided data collection and model training schemes for mTSS prediction and showed an important contribution to building an automated scoring system.
我们提出了一种腕关节半脱位/僵硬的分类模型，用于 X 射线图像的自动放射评分系统。在管理类风湿关节炎时，评估关节破坏很重要。改良总 Sharp 评分（mTSS）常用于评估手和脚的关节破坏，理想情况下应该自动化，因为所需的时间取决于评估者的技能，并且评估者之间存在差异。由于关节半脱位和僵硬在 mTSS 中会得到很高的评分，我们的目标是使用深度神经网络来估计半脱位和僵硬，作为开发关节破坏自动放射评分系统的第一步。我们从电子病历系统中随机抽取了 216 张手部 X 射线图像用于学习实验。这些图像是从 2015 年访问庆应义塾大学医院风湿病科的患者中获取的。使用我们新开发的注释工具，经过良好培训的风湿病学家和放射科医生对图像中包含的腕关节、掌指关节和近节指间关节进行了 mTSS 标记。我们确定了 21 张包含一个或多个半脱位关节的 X 射线图像和 42 张有僵硬的 X 射线图像。为了预测半脱位/僵硬，我们使用深度神经网络模型（AlexNet、ResNet、DenseNet 和 Vision Transformer）进行了五折交叉验证。腕关节半脱位/僵硬分类的最佳性能如下：准确性、精确性、召回率、F1 值和 AUC 分别为 0.97±0.01/0.89±0.04、0.92±0.12/0.77±0.15、0.77±0.16/0.71±0.13、0.82±0.11/0.72±0.09 和 0.92±0.08/0.85±0.07。基于深度神经网络的分类模型使用相对较小的数据集进行训练，但表现出了良好的准确性。总之，我们提供了 mTSS 预测的数据收集和模型训练方案，并为构建自动评分系统做出了重要贡献。
