School of Safety Engineering, China University of Mining and Technology, Xuzhou, People's Republic of China.
Xuzhou Engineering Research Center for Occupational Dust Control and Environmental Protection, Xuzhou, People's Republic of China.
J Breath Res. 2022 Apr 7;16(3). doi: 10.1088/1752-7163/ac5f13.
This study aims to develop an engineering solution to breath tests using an electronic nose (e-nose), and evaluate its diagnosis accuracy for silicosis. Influencing factors of this technique were explored. 398 non-silicosis miners and 221 silicosis miners were enrolled in this cross-sectional study. Exhaled breath was analyzed by an array of 16 organic nanofiber sensors along with a customized sample processing system. Principal component analysis was used to visualize the breath data, and classifiers were trained by two improved cost-sensitive ensemble algorithms (random forest and extreme gradient boosting) and two classical algorithms (K-nearest neighbor and support vector machine). All subjects were included to train the screening model, and an early detection model was run with silicosis cases in stage I. Both 5-fold cross-validation and external validation were adopted. Difference in classifiers caused by algorithms and subjects was quantified using a two-factor analysis of variance. The association between personal smoking habits and classification was investigated by the chi-square test. Classifiers of ensemble learning performed well in both screening and early detection model, with an accuracy range of 0.817-0.987. Classical classifiers showed relatively worse performance. Besides, the ensemble algorithm type and silicosis cases inclusion had no significant effect on classification (> 0.05). There was no connection between personal smoking habits and classification accuracy. Breath tests based on an e-nose consisted of 16× sensor array performed well in silicosis screening and early detection. Raw data input showed a more significant effect on classification compared with the algorithm. Personal smoking habits had little impact on models, supporting the applicability of models in large-scale silicosis screening. The e-nose technique and the breath analysis methods reported are expected to provide a quick and accurate screening for silicosis, and extensible for other diseases.
本研究旨在开发一种基于电子鼻（e-nose）的呼气测试工程解决方案，并评估其对矽肺的诊断准确性。探讨了该技术的影响因素。这项横断面研究纳入了 398 名非矽肺矿工和 221 名矽肺矿工。采用定制的样本处理系统，通过 16 个有机纳米纤维传感器阵列对呼气进行分析。采用主成分分析可视化呼吸数据，并采用两种改进的成本敏感集成算法（随机森林和极端梯度提升）和两种经典算法（K 近邻和支持向量机）训练分类器。所有受试者均纳入筛查模型训练，采用 I 期矽肺病例运行早期检测模型。采用 5 折交叉验证和外部验证。采用双因素方差分析量化算法和受试者引起的分类器差异。采用卡方检验研究个人吸烟习惯与分类的关系。集成学习分类器在筛查和早期检测模型中表现良好，准确率范围为 0.817-0.987。经典分类器表现相对较差。此外，集成算法类型和矽肺病例纳入对分类没有显著影响（>0.05）。个人吸烟习惯与分类准确率之间没有联系。基于电子鼻的 16×传感器阵列的呼气测试在矽肺筛查和早期检测中表现良好。与算法相比，原始数据输入对分类的影响更为显著。个人吸烟习惯对模型的影响较小，支持模型在大规模矽肺筛查中的适用性。电子鼻技术和报告的呼吸分析方法有望为矽肺提供快速准确的筛查，并可扩展用于其他疾病。
