Introduction

Although rapid screening for and diagnosis of COVID-19 are still urgently needed, standard testing methods are long and costly. Real-time mass spectrometry (MS) breath analysis is a non-invasive, point-of-care technique based on the detection of volatile organic compounds which can be a tool of interest.

Aims and objectives

To determine whether artificial-intelligence-enhanced real-time MS breath analysis is a reliable, safe, rapid means of screening ambulatory patients for COVID-19.

Methods

In two prospective, open, interventional studies in a single university hospital, we used real-time, proton transfer reaction time-of-flight MS to perform a metabolomic analysis of exhaled breath from adults requiring screening for COVID-19. Artificial intelligence and machine learning techniques were used to build mathematical models based on breath analysis data either alone or combined with patient metadata.

Results

We obtained breath samples from 173 participants, of whom 67 had proven COVID-19. After processing breath analysis data and further enhancing the machine learning model by adding patient metadata, our method was able to differentiate between COVID-19-positive and -negative participants (sensitivity: 98%, specificity: 74%, negative predictive value: 98%, positive predictive value: 72%, area under the ROC curve: 0.961). The predictive performance was similar for asymptomatic, weakly symptomatic and symptomatic participants and was not biased by the COVID-19 vaccination status.

Conclusions

Real-time, non-invasive, artificial-intelligence-enhanced mass spectrometry breath analysis might be a reliable, safe, rapid, cost-effective, high-throughput method for COVID-19 screening.