基于唾液样本拉曼光谱的机器学习驱动诊断的集成计算管道
Dario Bertazioli1, Marco Piazza1, Cristiano Carlomagno2
1University of Milano-Bicocca, Viale Sarca 336, Milan, 20126, Italy.
Computers in biology and medicine
|February 9, 2024
概括
深度学习分析了唾液中的拉曼光谱来检测疾病. 这种非侵入性方法对快速查感染如SARS-CoV-2和神经退行性疾病有希望.
科学领域:
- 生物医学光谱学 生物医学光谱学
- 计算生物学 计算生物学
- 医学诊断 医学诊断 医学诊断
背景情况:
- 拉曼光谱在生物样本中提供疾病特定的光谱指纹.
- 对专家来说,解释复杂的光谱数据仍然是一个重大挑战.
- 目前的诊断方法可能是侵入性的,昂贵的或耗时的.
研究的目的:
- 开发和验证一个端到端的深度学习管道,用于分类拉曼光谱数据.
- 评估管道在诊断SARS-CoV-2感染中的有效性.
- 评估查神经退行性疾病的管道,包括帕金森氏症和阿尔茨海默氏症.
主要方法:
- 利用端到端的深度学习架构来处理来自唾液样本的拉曼光谱数据.
- 从已确认的SARS-CoV-2阳性和阴性个体的光谱数据集上培训和验证了管道.
- 在帕金森病和阿尔茨海默病患者的光谱数据上测试了管道的性能.
主要成果:
- 深度学习管道成功分类了用于疾病检测的光谱数据.
- 在区分健康对照和患有SARS-CoV-2感染的患者方面表现出高准确度.
- 在查神经退行性疾病 (如帕金森病和阿尔茨海默病) 中显示出有希望的结果.
结论:
- 一个端到端的深度学习方法可以有效地解码用于疾病诊断的拉曼光谱指纹.
- 开发的管道为疾病查提供了一个非侵入性,成本高效和时间高效的解决方案.
- 这项技术可为各种医疗条件的先进诊断测试提供快速原型.
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