In this paper, we explore the potential of using the multivoxel proton magnetic resonance spectroscopy (¹H-MRS) to diagnose neuropsychiatric systemic lupus erythematosus (NPSLE) with the assistance of a support vector machine broad learning system (BL-SVM). We retrospectively analysed 23 confirmed patients and 16 healthy controls, who underwent a 3.0 T magnetic resonance imaging (MRI) sequence with multivoxel ¹H-MRS in our hospitals. One hundred and seventeen metabolic features were extracted from the multivoxel ¹H-MRS image. Thirty-three metabolic features selected by the Mann-Whitney test were considered to have a statistically significant difference (). However, the best accuracy achieved by conventional statistical methods using these 33 metabolic features was only 77%. We turned to develop a support vector machine broad learning system (BL-SVM) to quantitatively analyse the metabolic features from ¹H-MRS. Although not all the individual features manifested statistics significantly, the BL-SVM could still learn to distinguish the NPSLE from the healthy controls. The area under the receiver operating characteristic curve (AUC), the sensitivity, and the specificity of our BL-SVM in predicting NPSLE were 95%, 95.8%, and 93%, respectively, by 3-fold cross-validation. We consequently conclude that the proposed system effectively and efficiently working on limited and noisy samples may brighten a noinvasive in vivo instrument for early diagnosis of NPSLE.

中文翻译：

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广泛学习增强型 1H-MRS 用于神经精神系统性红斑狼疮的早期诊断

在本文中，我们探索了在支持向量机广泛学习系统 (BL-SVM) 的帮助下，使用多体素质子磁共振波谱 ( ¹ H-MRS) 诊断神经精神系统性红斑狼疮 (NPSLE)的潜力。我们回顾性分析了 23 名确诊患者和 16 名健康对照者，他们在我们医院接受了多体素¹ H-MRS的 3.0 T 磁共振成像 (MRI) 序列。从多体素¹ H-MRS 图像中提取了 117 个代谢特征。Mann-Whitney检验选择的 33 个代谢特征被认为具有统计学显着差异（）。然而，使用这 33 种代谢特征的传统统计方法所达到的最佳准确率仅为 77%。我们转而开发支持向量机广泛学习系统 (BL-SVM) 来定量分析¹ H-MRS的代谢特征。虽然并不是所有的个体特征都表现出显着的统计数据，但 BL-SVM 仍然可以学会区分 NPSLE 和健康对照。通过 3 倍交叉验证，我们的 BL-SVM 预测 NPSLE 的受试者工作特征曲线下面积 (AUC)、敏感性和特异性分别为 95%、95.8% 和 93%。因此，我们得出结论，所提出的系统有效且高效地处理有限和嘈杂的样本可能会使体内无创 NPSLE 的早期诊断工具。