机器学习使用呼气分析来检测COVID-19肺炎:一项概念验证研究
Ruth P Cusack1,2, Robyn Larracy3, Christian B Morrell3
1Department of Respiratory Medicine, Galway University Hospital, Galway, Ireland.
Journal of breath research
|February 21, 2024
概括
一种新的激光吸收光谱 (LAS) 呼吸测试显示了检测严重急性呼吸系统综合征冠状病毒-2 (SARS-CoV-2) 的前景. 这种非侵入性方法可以改善COVID-19肺炎的早期诊断.
科学领域:
- 呼吸系统医学 呼吸系统医学
- 分析化学 分析化学
- 计算生物学 计算生物学
背景情况:
- 对SARS-CoV-2的实时逆转录酶聚合酶连锁反应 (RT-PCR) 有局限性,包括低呼吸道感染的高假阴性率.
- 对于早期COVID-19肺炎诊断,需要一种安全,简单和可访问的下呼吸道采样方法.
研究的目的:
- 评估激光吸收光谱 (LAS) 分析呼气气迹的有效性,结合机器学习 (ML),用于检测SARS-CoV-2.
主要方法:
- 一项前性观察性研究招募了确诊SARS-CoV-2的患者和对照组.
- 使用LAS分析呼吸样本以检测挥发性有机化合物 (VOCs).
- 机器学习模型分类了独特的LAS光谱模式 (呼吸痕迹) 来识别SARS-CoV-2.
主要成果:
- 使用ML的LAS-breathprints在区分SARS-CoV-2阳性和阴性个体方面实现了72.2%-81.7%的准确性.
- 在各种患者亚组和SARS-CoV-2变种中,表现保持一致.
- 这种方法的性能优于经过VOC训练的分类器模型 (63%-74.7%的准确性).
结论:
- 基于ML的LAS呼吸分析为检测SARS-CoV-2和其他潜在的呼吸道病原体提供了一种有价值的非侵入性方法.
- 这项技术易于部署,可扩展和多功能,用于快速检测和未来疫情准备.
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