机器学习用于使用表面增强拉曼光谱测定COVID-19的确定
Tomasz R Szymborski1, Sylwia M Berus1, Ariadna B Nowicka2
1Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland.
Biomedicines
|January 23, 2024
概括
表面增强拉曼光谱 (SERS) 与机器学习 (ML) 结合,提供了一种快速,低成本的检测SARS-CoV-2的方法. 这种方法可以准确地识别临床样本中的病毒,如唾液和拭子.
科学领域:
- 生物医学工程 生物医学工程
- 分析化学 分析化学
- 传染病诊断 传染病诊断 传染病诊断
背景情况:
- 快速和经济有效地检测SARS-CoV-2对于临床管理至关重要.
- 当前的诊断方法在速度和可访问性方面面临挑战.
- 光谱技术与化学测量技术相结合,提供了一个有希望的替代方案.
研究的目的:
- 评估表面增强拉曼光谱 (SERS) 与机器学习 (ML) 结合用于检测SARS-CoV-2的有效性.
- 评估SERS-ML在分析临床样本 (如唾液和鼻拭片) 的性能.
- 确定这种方法在实际临床应用中的可行性.
主要方法:
- 用表面增强的拉曼光谱法 (SERS) 来分析唾液和鼻拭片样本.
- 机器学习算法,包括随机森林 (RF),被用于光谱数据分析.
- 调查了175个唾液和114个鼻口腔抽样的队列.
主要成果:
- 随机森林分类器在分析SERS光谱方面表现出很高的性能.
- 对于唾液样本,随机森林模型实现了94.0%的精度和88.9%的回忆率.
- 即使在有限数量的临床样本中,SERS-ML方法也显示出有效性.
结论:
- SERS和浅层机器学习的整合为SARS-CoV-2检测提供了一个可行的策略.
- 这种方法有可能在临床环境中快速,低成本和高效地识别SARS-CoV-2.
- 进一步开发可以加强其在传染病诊断中的作用.
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