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对于脏疾病的非侵入性诊断方法:电子鼻子系统的作用

Francesco Sansone¹, Alessandro Tonacci¹

¹Institute of Clinical Physiology, National Research Council of Italy (IFC-CNR), 56124 Pisa, Italy.

Sensors (Basel, Switzerland)

|October 16, 2024

概括

电子鼻子通过分析呼吸中的氨等化合物来检测脏疾病,这是一种微创的方法. 这项技术与机器学习相结合,显示出早期诊断和监测病的前景.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
分析化学分析化学
生物医学工程生物医学工程

背景情况:

脏疾病损害了血液过过程,导致有毒物质的积累和特征性呼吸变化,如氨气味.
早期发现功能障碍对于管理健康影响和预防疾病进展至关重要.
需要最小入侵的诊断工具来减少患者负担并改善临床见解.

研究的目的:

审查电子鼻子 (e-nose) 技术用于检测脏疾病的应用.
分析呼吸中的化合物检测背后的化学和物理原理,特别是氨.
评估用于电子鼻子数据处理用于病评估的机器学习方法.

主要方法:

对过去20年发表的关于人类主题的文章进行系统的文献综述.
遵守PRISMA指南的研究选择和数据提取.
专注于电子鼻子传感器技术,检测原理和数据分析技术.

主要成果:

电子鼻子可以检测出与病相关的气息中的挥发性有机化合物,主要是氨.
机器学习算法被广泛用于分析电子鼻子数据和分类疾病状态.
审查的研究表明,电子鼻子技术作为一种非侵入性诊断辅助工具的潜力.

结论:

电子鼻子技术与机器学习相结合,为非侵入性病检测提供了一个有希望的途径.
需要进一步的研究和技术进步来完善传感器的灵敏度和数据解释,以便在临床应用中使用.
这种方法可以显著帮助早期诊断,监测和管理相关的健康状况.

关键词:

在GC-MS中使用GC-MS.挥发性有机化合物挥发性有机化合物人工智能的人工智能是人工智能.呼吸分析呼吸分析电子鼻子电子鼻子电子鼻子电子鼻子脏疾病脏疾病模式识别模式识别模式识别挥发性有机化合物挥发性有机化合物飞行生物学飞行生物学

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