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

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.