COVID-19 screening using breath-borne volatile organic compounds
Haoxuan Chen, Qi Xiao, Lu Zhang, Xinyue Li, Jianxin Ma, Chun‐yang Zhang, Huasong Feng, Maosheng Yao
Abstract
= 87). In contrast, breath-borne acetone was found to be significantly lower for COVID-19 patients than other subjects. Twelve key endogenous VOC species using supervised machine learning models (support vector machines, gradient boosting machines (GBMs), and Random Forests) were shown to exhibit strong capabilities in discriminating COVID-19 from (HCW + NC) and RI with a precision ranging from 91% to 100%. GBM and Random Forests models can also discriminate RI patients from healthy subjects with a precision of 100%. In addition, the developed models using breath-borne VOCs could also detect a confirmed COVID-19 patient but with a false negative throat swab polymerase chain reaction test. It takes 10 min to allow an entire breath test to finish, including analysis of the 12 key VOC species. The developed technology provides a novel concept for non-invasive rapid point-of-care-test screening for COVID-19 in various scenarios.