ABSTRACT
Background A new coronavirus, SARS-CoV-2, has caused the coronavirus disease-2019 (COVID-19) epidemic. Current diagnostic methods mainly include nucleic acid detection, antibody detection, antigen detection, and chest computed tomography (CT) imaging. Although these methods are crucial for the diagnosis of COVID-19, there is a lack of a rapid and economical method for preliminary screening COVID-19.Methods We measured the FeNO concentrations of 103 subjects without COVID-19 and 46 patients with COVID-19. Using machine learning (ML) method, we build a ML model based on fractional exhaled nitric oxide (FeNO) concentration and features of age, and body size for rapid preliminary screening COVID-19 suspects with low-cost.Findings The statistical analysis t-test show that there is a significant difference between the FeNO of healthy people and patients with COVID-19. The ML model can screen out the patients with COVID-19 or other diseases, which show abnormal FeNO distributions. An area under the curve of 0.982 and a sensitivity 0.917 have been achieved for preliminary screening COVID-19 suspects. This non-invasive detection method which takes in two minutes and costs less than a dollar could provide a direction for the control of the rapid spread COVID-19.Interpretation During the COVID-19 pandemic, large numbers and extensive testing of COVID-19 patients remains a problem. Public healthy efforts to limit SARS-CoV-2 spread need to find a more economical and faster screening method.