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使用 ML 驱动的低成本传感器阵列进行基于呼吸的肺癌检测.

Dhruv Iyer¹, Kavin Gobinath², Krish Kowkuntla²

¹Mountain View High School, Mountain View, 94040, US.

Scientific reports

|November 25, 2025

概括

这项研究介绍了一种负担得起的电子鼻子 (e-nose) 设备,用于快速,非侵入性的肺癌检测. 该系统实现了高精度,超过了现有的早期查方法.

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科学领域:

生物医学工程生物医学工程
分析化学分析化学
在瘤学瘤学.

背景情况:

肺癌是全球癌症死亡的主要原因之一.
非侵入性检测方法对于早期诊断至关重要.
目前用于肺癌检测的电子鼻子 (e-nose) 系统在准确性和速度方面存在局限性.

研究的目的:

开发一种负担得起且准确的电子鼻子设备,用于非侵入性肺癌检测.
通过解决精度和检测时间的局限性来提高电子鼻子系统的性能.
评估一种新型数据增强技术的有效性,以增强电子鼻子分析.

主要方法:

开发了一种负担得起的电子鼻子设备,配有12个金属氧化物半导体传感器和1个化学抗性烯传感器,能够检测30多种挥发性有机化合物.
从28名健康对照组和18名肺癌患者收集了呼吸样本.
利用多层感知神经网络进行数据分析,并使用基于高斯噪声的数据增强来扩展数据集.
进行了5倍的交叉验证,用于模型评估.

主要成果:

电子鼻子系统实现了高诊断性能:96.26%的准确性,92.88%的灵敏性,97.75%的特异性和0.9286.6的AUC.
与现有的电子鼻子检测方法相比,开发的系统表现出了超过5%的性能改善.
呼吸样本的分类大约在5分钟内完成.

结论:

开发的电子鼻子系统显示出作为一种快速,经济高效的工具,用于初步肺癌查的巨大潜力.
数据增强技术的整合提高了基于电子鼻子的诊断的准确性和可靠性.
这项技术提供了一种有前途的非侵入性方法,以补充现有的肺癌诊断策略.