基于拉曼和NIR光谱的风湿性和牛皮性关节炎的歧视,使用机器学习算法
Przemysław Cuprych1, Izabela Kokot2, Roman Szostak1
1Department of Chemistry, University of Wroclaw, F. Joliot-Curie, 50-383 Wroclaw, Poland.
Molecules (Basel, Switzerland)
|December 11, 2025
概括
振动光谱学有效地区分类风湿性关节炎 (RA) 和牛皮性关节炎 (PsA),使用血清光谱. 这种方法有助于诊断这些自身免疫性疾病,这些疾病具有相似的症状,缺乏特定的标志物.
科学领域:
- 生物医学光谱学 生物医学光谱学
- 化学测量 化学测量 化学测量
- 自免疫性疾病的诊断 自免疫性疾病的诊断
背景情况:
- 类风湿性关节炎 (RA) 和牛皮性关节炎 (PsA) 是具有重叠症状的慢性自身免疫性疾病.
- 缺乏特定的生物标志物使准确的诊断变得复杂,导致潜在的错误分类.
- 体液组成的光谱分析为疾病分化提供了一个潜在的方法.
研究的目的:
- 评估拉曼光谱和近红外光谱 (NIR) 与化学测量方法相结合的疗效,以区分RA和PSA.
- 为了比较部分最小平方差分分析 (PLS-DA) 和反传播人工神经网络 (CP-ANN) 模型的性能.
- 评估使用血清光谱数据和生化参数的模型的诊断准确性.
主要方法:
- 使用拉曼和NIR光谱学分析RA (n=30),PsA (n=24) 和健康对照 (HC,n=15) 的冷血清.
- 区间部分最小平方 (iPLS) 的应用用于光谱特征选择.
- 开发和验证PLS-DA和CP-ANN模型,包括结合生物化学数据的混合模型.
主要成果:
- 基于选定的光谱特征的PLS-DA和CP-ANN模型实现了81.3-93.8%的整体精度 (OA) 来区分RA,PSA和HC.
- 结合光谱变量和生化参数的混合模型显示OA值高,从87.5%到93.8%.
- 该研究成功地区分了RA和PsA,使用来自干血清的光谱数据.
结论:
- 振动光谱学 (拉曼和NIR) 与化学测量建模相结合,为区分RA和PsA提供了可靠的方法.
- 开发的模型显示了改善这些具有挑战性的自身免疫性疾病的诊断的巨大潜力.
- 血清的光谱分析提供了一个非侵入性和有效的策略,以区分RA和PsA.
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