Wang Hao-Jan, Su Chi-Ping, Lai Chien-Chih, Chen Wun-Rong, Chen Chi, Ho Liang-Ying, Chu Woei-Chyn, Lien Chung-Yueh
Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei City 112, Taiwan.
Division of Allergy, Immunology, and Rheumatology, Department of Medicine, Taipei Veterans General Hospital, Taipei City 112, Taiwan.
Biomedicines. 2022 Jun 8;10(6):1355. doi: 10.3390/biomedicines10061355.
Rheumatoid arthritis (RA) is a systemic autoimmune disease; early diagnosis and treatment are crucial for its management. Currently, the modified total Sharp score (mTSS) is widely used as a scoring system for RA. The standard screening process for assessing mTSS is tedious and time-consuming. Therefore, developing an efficient mTSS automatic localization and classification system is of urgent need for RA diagnosis. Current research mostly focuses on the classification of finger joints. Due to the insufficient detection ability of the carpal part, these methods cannot cover all the diagnostic needs of mTSS.
We propose not only an automatic label system leveraging the You Only Look Once (YOLO) model to detect the regions of joints of the two hands in hand X-ray images for preprocessing of joint space narrowing in mTSS, but also a joint classification model depending on the severity of the mTSS-based disease. In the image processing of the data, the window level is used to simulate the processing method of the clinician, the training data of the different carpal and finger bones of human vision are separated and integrated, and the resolution is increased or decreased to observe the changes in the accuracy of the model.
Integrated data proved to be beneficial. The mean average precision of the proposed model in joint detection of joint space narrowing reached 0.92, and the precision, recall, and F1 score all reached 0.94 to 0.95. For the joint classification, the average accuracy was 0.88, and the accuracy of severe, mild, and healthy reached 0.91, 0.79, and 0.9, respectively.
The proposed model is feasible and efficient. It could be helpful for subsequent research on computer-aided diagnosis in RA. We suggest that applying the one-hand X-ray imaging protocol can improve the accuracy of mTSS classification model in determining mild disease if it is used in clinical practice.
类风湿性关节炎（RA）是一种全身性自身免疫性疾病；早期诊断和治疗对其管理至关重要。目前，改良总夏普评分（mTSS）被广泛用作RA的评分系统。评估mTSS的标准筛查过程繁琐且耗时。因此，开发一种高效的mTSS自动定位和分类系统对于RA诊断迫在眉睫。当前研究大多集中在手指关节的分类上。由于腕部区域检测能力不足，这些方法无法满足mTSS的所有诊断需求。
我们不仅提出了一种利用你只看一次（YOLO）模型的自动标注系统，用于在手X线图像中检测双手关节区域，以对mTSS中的关节间隙变窄进行预处理，还提出了一种基于mTSS疾病严重程度的关节分类模型。在数据的图像处理中，使用窗宽窗位来模拟临床医生的处理方法，分离并整合人类视觉中不同腕骨和指骨的训练数据，并调整分辨率以观察模型准确性的变化。
整合数据被证明是有益的。所提出模型在关节间隙变窄的关节检测中的平均精度达到0.92，精确率、召回率和F1分数均达到0.94至0.95。对于关节分类，平均准确率为0.88，重度、轻度和健康的准确率分别达到0.91、0.79和0.9。
所提出的模型是可行且高效的。它可能有助于后续RA计算机辅助诊断的研究。我们建议，如果在临床实践中应用单手X线成像方案，可以提高mTSS分类模型在确定轻度疾病时的准确性。
