Engineering solutions to breath tests based on an e-nose system for silicosis screening and early detection in miners

Abstract

Objectives: 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. Methods: 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 (RF and XGBoost) and two classical algorithms (KNN and SVM). 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. Results: Classifiers of ensemble learning performed well in both screening and early detection model, with an accuracy range of 0.817 to 0.987. Classical classifiers showed relatively worse performance. Besides, the ensemble algorithm type and silicosis cases inclusion had no significant effect on classification (p>0.05). There was no connection between personal smoking habits and classification accuracy. Conclusion: Breath tests based on an e-nose consisted of 16x 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.

Room-Temperature Smart Sensor Based on Indium Acetate-Functionalized Perovskite CsPbBr3 Nanocrystals for Monitoring Electrolyte in Lithium-Ion Batteries

“…The VGG16 and ResNet18 model network architecture details are provided in the Supporting Information. Using the strategy of natural learning rate reduction, − it can be seen that the loss value of the VGG16 model basically remains stable after undergoing 60 epoch training with no significant increase in accuracy. And at the end of the training, it showed a greater loss state .…”

Differentiating interstitial lung diseases from other respiratory diseases using electronic nose technology

“…No studies have been published on early detection of ILD using an eNose, except for two studies that focus on pneumoconiosis screening in high risk groups [ 15 , 16 ]. Although these were pilot studies, they found high accuracies when comparing people with and without pneumoconiosis.…”

“…In ILD, breath profiles of patients could be differentiated from healthy controls [ 10 – 14 ] and individual ILDs from COPD [ 11 , 12 ]. Exploratory studies in pneumoconiosis show the potential of using an eNose for screening purposes in ILD [ 15 , 16 ].…”

Machine Learning-Assisted Sensor Based on CsPbBr3@ZnO Nanocrystals for Identifying Methanol in Mixed Environments

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