对最近应用于FTIR光谱数据分析人类血细胞的机器学习方法的审查
Ahmed Fadlelmoula1,2, Susana O Catarino1,2, Graça Minas1,2
1Center for Microelectromechanical Systems (CMEMS-UMinho), Campus de Azurém, University of Minho, 4800-058 Guimarães, Portugal.
Micromachines
|June 28, 2023
概括
机器学习 (ML) 和里埃变换红外光谱 (FTIR) 显示出对分析人类血细胞的前景. 本综述强调了SVM和PCA等常见的ML方法,用于区分健康细胞和病态细胞.
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:
- 机器学习 (ML) 为数据分析提供了强大的工具,使大数据库能够快速转化为临床应用.
- 福里埃变换红外光谱 (FTIR) 从生物样本 (包括人类血液) 中提供丰富的生物化学信息.
- 结合FTIR和ML具有很大的潜力,可以提高血液学中的诊断能力.
研究的目的:
- 审查和综合研究的应用ML技术与FTIR光谱对人类血液分析.
- 确定用于区分病态和健康人体血细胞的常见ML算法和验证策略.
- 评估当前的情况,并确定需要改进的ML驱动FTIR血液分析领域.
主要方法:
- 对2019-2023年期间的出版物进行了系统的文献审查.
- 搜索了使用ML算法与FTIR光谱学结合用于人类血液分析的研究.
- 根据研究设计,统计方法以及报告的优点和局限性,评估了39个已识别的出版物.
主要成果:
- 总共有39个相关出版物被确定和审查.
- 支持矢量机 (SVM) 和主要组件分析 (PCA) 是最常用的ML方法.
- 大多数研究都使用了内部验证和多个ML算法,只有四项研究使用了单个算法.
结论:
- 目前正在使用各种ML方法,统计软件和验证策略.
- 需要明确定义的模型选择策略和一致应用内部和外部验证.
- 标准化方法对于确保使用ML和FTIR对人类血细胞进行可靠和有效的歧视至关重要.
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