Diagnostische Herausforderungen bei Mischstaub-Pneumokoniosen – 5 Fallbeispiele

Silicosis is a fibrotic lung disease caused by inhalation of silica dusts, early and accurate diagnosis of which remains a challenge. We aimed to assess the performance of a nanofiber sensor array and pattern recognition to promptly and noninvasively detect silicosis. A total of 210 silicosis cases and 430 non-silicosis controls were enrolled in a cross-sectional study. Exhaled breath was analysed by a portable analytical system incorporating an array of 16x organic nanofiber sensors. Models were established by Deep Neural Network and eXtreme Gradient Boosting. Linear Discriminant Analysis was used for dimensionality reduction and visualized data analysis. Receiver Operating Characteristic Curve, accuracy, sensitivity and specificity were used to evaluate models. Results: 99.3% AUC, 96.0% accuracy, 94.1% sensitivity, and 96.3% specificity were achieved in test set. Silicosis cases present different breath patterns from healthy controls, classification results using which were highly consistent with the experts’ diagnosis. Breath analysis performed with the sensor array and pattern recognition is expected to provide a quick, stable recognition for silicosis. In this paper, different forms of features, different algorithms and data sets over long time periods were used, which provides a reference for silicosis expiratory diagnosis scheme.

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

Xuan¹,

Zheng²,

Bunes³

et al. 2022

J. Breath Res.

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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.

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Diagnostische Herausforderungen bei Mischstaub-Pneumokoniosen – 5 Fallbeispiele

Cited by 3 publications

References 22 publications

Prevalence of Pneumoconiosis in the Construction Industry: A Systematic Review

Prevalence of Pneumoconiosis in the Construction Industry: A Systematic Review

Non-invasive detection of silicosis based on array sensing and pattern recognition

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

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