Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Academy for Advanced Interdisciplinary Studies, and State Key Laboratory of Natural and Biomimetic Drugs, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing, 100871, China.
Department of Laboratory Medicine, Peking University Third Hospital, No. 49 North Garden Road, Haidian District, Beijing, 100191, China.
Sci Rep. 2022 Jun 13;12(1):9810. doi: 10.1038/s41598-022-13750-9.
Rheumatoid arthritis (RA) is chronic systemic disease that can cause joint damage, disability and destructive polyarthritis. Current diagnosis of RA is based on a combination of clinical and laboratory features. However, RA diagnosis can be difficult at its disease onset on account of overlapping symptoms with other arthritis, so early recognition and diagnosis of RA permit the better management of patients. In order to improve the medical diagnosis of RA and evaluate the effects of different clinical features on RA diagnosis, we applied an artificial neural network (ANN) as the training algorithm, and used fivefold cross-validation to evaluate its performance. From each sample, we obtained data on 6 features: age, sex, rheumatoid factor, anti-citrullinated peptide antibody (CCP), 14-3-3η, and anti-carbamylated protein (CarP) antibodies. After training, this ANN model assigned each sample a probability for being either an RA patient or a non-RA patient. On the validation dataset, the F1 for all samples by this ANN model was 0.916, which was higher than the 0.906 we previously reported using an optimal threshold algorithm. Therefore, this ANN algorithm not only improved the accuracy of RA diagnosis, but also revealed that anti-CCP had the greatest effect while age and anti-CarP had a weaker on RA diagnosis.
类风湿关节炎（RA）是一种慢性全身性疾病，可导致关节损伤、残疾和破坏性多关节炎。目前的 RA 诊断基于临床和实验室特征的组合。然而，由于与其他关节炎的症状重叠，RA 在疾病发作时的诊断可能很困难，因此早期识别和诊断 RA 可以更好地管理患者。为了提高 RA 的医学诊断水平，并评估不同临床特征对 RA 诊断的影响，我们应用人工神经网络（ANN）作为训练算法，并使用五重交叉验证来评估其性能。从每个样本中，我们获得了 6 个特征的数据：年龄、性别、类风湿因子、抗瓜氨酸化肽抗体（CCP）、14-3-3η 和抗氨甲酰化蛋白（CarP）抗体。经过训练，该 ANN 模型为每个样本分配了一个概率，用于判断其是否为 RA 患者或非 RA 患者。在验证数据集上，该 ANN 模型对所有样本的 F1 值为 0.916，高于我们之前使用最佳阈值算法报告的 0.906。因此，该 ANN 算法不仅提高了 RA 诊断的准确性，还揭示了抗 CCP 对 RA 诊断的影响最大，而年龄和抗 CarP 的影响较弱。
