ArticleEClinicalMedicine2021
A method for the identification of COVID-19 biomarkers in human breath using Proton Transfer Reaction Time-of-Flight Mass Spectrometry.
Article in EClinicalMedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A comprehensive meta-analysis and systematic review of breath analysis in detection of COVID-19 through Volatile organic compounds.Diagnostic microbiology and infectious disease · 2024Pooled it
- Real-Time Breath Diagnostics: Linking Molecular Pathways, Measurement Technologies, and Clinical Translation.International journal of molecular sciences · 2026Review
- Establishment and validation of an Alzheimer's disease diagnostic model on the basis of exhaled volatile organic compound characteristics.Translational psychiatry · 2026Article
- Nanobiosensors: A Potential Tool to Decipher the Nexus Between SARS-CoV-2 Infection and Gut Dysbiosis.Sensors (Basel, Switzerland) · 2026Review
- Opportunities and Challenges in Gas Sensor Technologies for Accurate Detection of COVID-19.Biosensors · 2025Review
- Examination of unique volatile organic compound signatures in nasopharyngeal test swab viral transport media using an electronic nose.PloS one · 2025Article
- A Comprehensive Review of Biomarker Sensors for a Breathalyzer Platform.Sensors (Basel, Switzerland) · 2024Review
- Detection of COVID-19 by quantitative analysis of carbonyl compounds in exhaled breath.Scientific reports · 2024Article
- A feasibility study on exhaled breath analysis using UV spectroscopy to detect COVID-19.Journal of breath research · 2023Article
- Enhanced real-time mass spectrometry breath analysis for the diagnosis of COVID-19.ERJ open research · 2023Article
- Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers.Sensors (Basel, Switzerland) · 2023Review
- Exhaled Biomarkers for Point-of-Care Diagnosis: Recent Advances and New Challenges in Breathomics.Micromachines · 2023Review
- Research progress of electronic nose technology in exhaled breath disease analysis.Microsystems & nanoengineering · 2023Review
- Advanced setup for safe breath sampling and patient monitoring under highly infectious conditions in the clinical environment.Scientific reports · 2022Article
- Profiling of exhaled volatile organics in the screening scenario of a COVID-19 test center.iScience · 2022Article
- Forecasting the Post-Pandemic Effects of the SARS-CoV-2 Virus Using the Bullwhip Phenomenon Alongside Use of Nanosensors for Disease Containment and Cure.Materials (Basel, Switzerland) · 2022Review
- Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose.Artificial intelligence in medicine · 2022Article
- Detection of SARS-CoV-2 infection by exhaled breath spectral analysis: Introducing a ready-to-use point-of-care mass screening method.EClinicalMedicine · 2022Article
- The Impact of a Graded Maximal Exercise Protocol on Exhaled Volatile Organic Compounds: A Pilot Study.Molecules (Basel, Switzerland) · 2022Article
- Mass spectrometry for metabolomics analysis: Applications in neonatal and cancer screening.Mass spectrometry reviewsReview
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCOVID-19 has caused a worldwide pandemic, making the early detection of the virus crucial. We present an approach for the determination of COVID-19 infection based on breath analysis.
methodsA high sensitivity mass spectrometer was combined with artificial intelligence and used to develop a method for the identification of COVID-19 in human breath within seconds. A set of 1137 positive and negative subjects from different age groups, collected in two periods from two hospitals in the USA, from 26 August, 2020 until 15 September, 2020 and from 11 September, 2020 until 11 November, 2020, was used for the method development. The subjects exhaled in a Tedlar bag, and the exhaled breath samples were subsequently analyzed using a Proton Transfer Reaction Time-of-Flight Mass Spectrometer (PTR-ToF-MS). The produced mass spectra were introduced to a series of machine learning models. 70% of the data was used for these sub-models' training and 30% was used for testing.
findingsA set of 340 samples, 95 positives and 245 negatives, was used for the testing. The combined models successfully predicted 77 out of the 95 samples as positives and 199 out of the 245 samples as negatives. The overall accuracy of the model was 81.2%. Since over 50% of the total positive samples belonged to the age group of over 55 years old, the performance of the model in this category was also separately evaluated on 339 subjects (170 negative and 169 positive). The model correctly identified 166 out of the 170 negatives and 164 out of the 169 positives. The model accuracy in this case was 97.3%.
interpretationThe results showed that this method for the identification of COVID-19 infection is a promising tool, which can give fast and accurate results.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.