医学诊断中的机器学习辅助气体传感器阵列的审查
Yueting Yu1, Xin Cao1, Chenxi Li1
1School of Pharmaceutical Sciences and Institute of Materia Medica, Xinjiang University, Urumqi 830017, China.
Biosensors
|August 27, 2025
概括
机器学习辅助的气体传感器阵列通过检测呼吸中的挥发性有机化合物 (VOC) 来提供一种非侵入性疾病诊断方法. 这篇评论探讨了它们在各种疾病中的应用,强调了当前的挑战和未来的方向.
科学领域:
- 生物标志物和诊断
- 传感器技术
- 医学中的人工智能
背景情况:
- 呼气中的挥发性有机化合物是潜在的非侵入性疾病生物标志物.
- 传统的VOC检测方法成本高且复杂.
- 具有机器学习的气体传感器阵列为疾病诊断提供了可行的替代方案.
研究的目的:
- 系统地审查机器学习辅助的气体传感器阵列在医学诊断中的先进应用.
- 总结呼吸分析中使用的传感器类型和机器学习算法.
- 讨论使用这种技术诊断各种疾病的进展.
主要方法:
- 对传感器类型的审查:电化学,光学和半导体传感器.
- 机器学习算法的列表:支持矢量机器 (SVM),随机森林 (RF),人工神经网络 (ANN),主要组件分析 (PCA).
- 对诊断呼吸道,代谢,肝胆道,胃肠道和神经系统疾病的研究进展进行讨论.
主要成果:
- 机器学习可以从传感器阵列数据中提升复杂的VOC混合物的识别.
- 通过呼吸分析成功诊断了一系列疾病.
- 确定了用于医学诊断的关键传感器类型和算法.
结论:
- 机器学习辅助的气体传感器阵列对非侵入性疾病诊断具有显著的前景.
- 需要进一步的研究来应对当前的挑战,并优化未来的应用.
- 与传统方法相比,这种技术提供了一种成本效益较高且不那么繁的方法.
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