Abstract
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.
Highlights
Silicosis is one of the most critical occupational diseases worldwide [1, 2], which is incurable and becomes less treatable in late stages [3]
210 consecutive silicosis cases and 430 nonsilicosis controls were enrolled in this cross-sectional
Receiver operating characteristic (ROC) analysis of models in test sets constructed with different algorithms and data features are shown in Figure 4, which is a visualized showing of AUC
Summary
Silicosis is one of the most critical occupational diseases worldwide [1, 2], which is incurable and becomes less treatable in late stages [3]. Most of the breath studies relied on the conventional bench-top analytical systems, such as GC-MS. These devices are complicated and cumbersome, which need skilled workers to operate and are unsuitable as a point of-care tool. Despite of the relatively small cohort group, this study showed the potential of developing a non-invasive tool for screening pneumoconiosis. In this context, we used a portable system to discriminate silicosis from healthy miners via sensors array and pattern recognition in a cohort of 640 subjects. Some exploratory analysis was done to provide reference for potential clinical use
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