Applying the electronic nose for pre-operative SARS-CoV-2 screening

Abstract

Background Infection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis. Methods Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between − 1 and + 1, indicating the infection probability. Results 219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96. Conclusions The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery.

Proof of concept for real-time detection of SARS CoV-2 infection with an electronic nose

“…To this we provide a positive answer, providing proof of concept for VOC-based real-time detection of SARS CoV-2 infection. We note that results to this effect have also recently been obtained by others: In one study [ 28 ], the authors used a commercial metal-oxide-sensor-based eNose, albeit different from ours (Aeonose, The Aeonose Company, Zutphen, the Netherlands). They sampled oral exhaled breath from 219 participants, of which 57 were COVID-19 positive.…”

“…Beyond this, the primary weakness that limits our effort to the status of proof of concept alone is the level of false positives. Although as noted our statistically significant 66.7% true positive rate (or 75.8% for non-symptomatic) is not far off of RT-PCR true positive rates [ 28 ], and we obtain this result instantaneously rather than days later, our 57% false positive rate prevents this method from deployment in its current form. Thus, our result is a basic-science proof of concept, and not a clinical tool.…”

“…Much of the eNose diagnostics effort in the literature is focused on exhaled breath analysis. Such breath sampling and analysis has standard protocols [ 18 , 19 ], and has reached at achievements in cases such as identifying pneumonia [ 20 ], tuberculosis [ 11 , 21 ], asthma and COPD [ 22 ], respiratory infections [ 23 ] and lung malignancies [ 24 – 26 ], and recently indeed for COVID-19 [ 27 , 28 ]. Moreover, eNose measurements of exhaled breath may inform on non-respiratory conditions as well, such as neurodegenerative illnesses [ 15 ].…”

Biodiagnostics in an era of global pandemics—From biosensing materials to data management

“…Another type of e‐nose (Aeonose) was tested for its plausibility to screen out COVID‐19 positive patients before surgery. [ 72 ] This system is based on an array of three microhotplate metal oxide sensors: carbon monoxide, nitrogen dioxide, and VOC sensors that change their conductivity after exposure. The sensors operation temperature cycles between 260 and 340 °C, followed by machine learning (ML) methodology used for classifying the data, the results showing 86% sensitivity.…”

Detecting SARS-CoV-2 in the Breath of COVID-19 Patients

“…Several groups have reported SARS-CoV-2 positivity in samples of exhaled breath condensate (EBC) ( 21 – 25 ), and based on Ct values, Ma et al further showed that EBC-positive COVID-19 patients exhaled millions of SARS-CoV-2 RNA copies per hour ( 23 ). Standard diagnosis of viruses based on swabs or serum has been rapidly developed ( 17 , 26 – 29 ), while auxiliary methods, such as trace biomarkers of COVID-19 in exhalations, have also been explored ( 18 – 20 ). Nevertheless, there is an urgent need for novel methods to detect SARS-CoV-2 in the transmission path.…”