一个机器学习驱动的表面增强拉曼散射分析平台,用于无标签检测和识别胃病变
Fengsong Chen1, Yanhua Huang1, Yayun Qian2
1Department of Gastroenterology, Haimen People's Hospital, Nantong, 226000, People's Republic of China.
International journal of nanomedicine
|September 16, 2024
概括
这项研究引入了一种新的方法来检测胃病变使用表面增强拉曼散射 (SERS) 和机器学习. 这项技术准确地分类血清样本,为早期诊断胃病铺平了道路.
科学领域:
- 生物医学工程 生物医学工程
- 分析化学 分析化学
- 计算生物学 计算生物学
背景情况:
- 胃病变由于可变的恶性病变和检测困难而存在诊断挑战.
- 早期和准确的诊断对于有效的胃病变治疗和患者的结果至关重要.
研究的目的:
- 开发一种无标签,高度敏感的方法来分类不同程度的胃病变患者的血清.
- 将表面增强的拉曼散射 (SERS) 与机器学习相结合,以改进胃病变检测.
主要方法:
- 使用金色莲花形 (AuLS) 纳米阵列基板进行SERS血清测量.
- 采用主要组件分析 (PCA) 和基于最近邻居 (MLMNN) 的多局部媒介用于光谱数据分析和分类.
主要成果:
- 使用AuLS纳米阵列实现了快速,灵敏和无标签的血清光谱检测.
- 证明了高分类准确性 (97.5%),敏感性 (> 96.7%) 和特异性 (> 95.0%).
- 通过PCA加载图表识别了通过PCA加载图区分胃损伤程度的关键光谱特征.
结论:
- 建立了将SERS和机器学习整合到胃病变诊断中的基础.
- 这种方法可以实时诊断和识别胃病变.
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