快速,非侵入性呼吸分析,以提高使用质谱和可解释的机器学习来检测病的检测
Merryn J Baker1, Jeff Gordon1, Aruvi Thiruvarudchelvan2
1School of Chemistry, University of New South Wales, Sydney, New South Wales 2052, Australia.
Journal of breath research
|March 3, 2025
概括
现在可以通过快速,非侵入性呼吸试验来早期检测职业性肺部疾病 - - 病. 这种新方法分析挥发性有机化合物 (VOC),以改善疾病诊断和结果.
科学领域:
- 环境健康 环境健康
- 分析化学 分析化学
- 生物标志物 生物标志物
背景情况:
- 职业肺部疾病,特别是病,由于日益增加的人工石灰暴露,造成了全球健康的重大风险.
- 目前用于病的诊断方法往往无法在早期发现疾病,从而妨碍及时干预.
研究的目的:
- 研究大气压化学电离质谱法 (APCI-MS) 的潜力,以快速,非侵入性检测病.
- 在呼出的呼吸中识别特定的挥发性有机化合物 (VOC),表明有氧化.
- 开发和验证机器学习 (ML) 模型,以根据呼吸中的VOC资料准确地分类病.
主要方法:
- 使用APCI-MS.对31名病患者和60名健康对照者的呼气挥发性有机化合物 (VOC) 进行分析.
- 应用六种可解释的机器学习 (ML) 模型,包括极端梯度增强,并使用Shapley增量解释 (SHAP) 进行分类.
- 识别有助于诊断准确性的关键VOC特征.
主要成果:
- 极端梯度增强分类器实现了高诊断性能,接收机操作员特征曲线下的面积为0.933.
- 可能与白血-E3相关的m/z 442特征被确定为化症的显著预测因素.
- 每个样本的呼吸分析和测量过程在不到五分钟内完成.
结论:
- 基于APCI-MS的呼吸分析为早期和非侵入性诊断病提供了一个有希望的途径.
- 可解释的ML模型为与症相关的生物标志物提供了宝贵的见解.
- 这种快速的,非侵入性的方法具有大规模人口查和改善职业肺部疾病管理的潜力.
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