In Journal of breath research
BACKGROUND : The spread of COVID-19 results in an increasing incidence and mortality. The typical diagnosis technique for SARS-CoV-2 infection is RT-PCR, which is relatively expensive, time-consuming, professional, and suffered from false-negative results. A reliable, non-invasive diagnosis method is in urgent need for the rapid screening of COVID-19 patients and controlling the epidemic.
METHODS : Here we constructed an intelligent system based on the VOC biomarkers in human breath combined with machine learning models. The VOC profiles of 122 breath samples (65 of COVID-19 infections and 57 of controls) were identified with a portable gas chromatograph-mass spectrometer. Among them, eight VOCs exhibited significant differences (p<0.001) between the COVID-19 group and the control group. The cross-validation algorithm optimized support vector machine (SVM) model was employed for the prediction of COVID-19 infection.
RESULTS : The proposed SVM model performed a powerful capability in discriminating COVID-19 patients from healthy controls, with an accuracy of 97.3%, a sensitivity of 100%, a specificity of 94.1%, and a precision of 95.2%, and an F1 score of 97.6%. The SVM model was also compared with other common machine models, including artificial neural network, k-nearest neighbor, and logistic regression, and demonstrated obvious superiority in the prediction of COVID-19 infection. Furthermore, user-friendly software was developed based on the optimized SVM model.
CONCLUSION : The developed intelligent platform based on breath analysis provides a new strategy for the point-of-care screening of COVID and shows great potential in clinical application.
Xue Cuili, Xu Xiaohong, Liu Zexi, Zhang Yuna, Xu Yuli, Niu Jiaqi, Jin Han, Xiong Wujun, Cui Daxiang
2022-Nov-08
COVID-19 diagnosis, Portable GC-MS, breath analysis, support vector machine, volatile organic compound
