ArticleNPJ digital medicine2022
Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 97 citations in OpenAlex.
- 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
- Development of a rapid metal oxide semiconductor-based sensory system for noninvasive neonatal sepsis detection.Mikrochimica acta · 2026Article
- Genomic and clinical epidemiology of SARS-CoV-2 in Lebanon: a prospective multicenter study 2020-2024.BMC infectious diseases · 2026Article
- Opportunities and Challenges in Gas Sensor Technologies for Accurate Detection of COVID-19.Biosensors · 2025Review
- A Review on Long COVID Screening: Challenges and Perspectives Focusing on Exhaled Breath Gas Sensing.ACS sensors · 2025Review
- Observational
- Examination of unique volatile organic compound signatures in nasopharyngeal test swab viral transport media using an electronic nose.PloS one · 2025Article
- AI-driven 5G IoT e-nose for whiskey classification.Applied intelligence (Dordrecht, Netherlands) · 2025Article
- Folded flexure MOEMS for the detection of PSA and hepatitis DNA as biosensor for prostate cancer and viruses.Scientific reports · 2024Article
- Utility, feasibility, and socio-demographic considerations in the diagnosis of bacterial RTI's by GC-IMS breath analysis.iScience · 2024Article
- Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array.Nano-micro letters · 2024Review
- Ultratrace eNose Sensing of VOCs toward Breath Analysis Applications Utilizing an eNose-Based Analyzer.ACS measurement science au · 2024Article
- Electronic Tongues and Noses: A General Overview.Biosensors · 2024Review
- Detection of Water Contaminants by Organic Transistors as Gas Sensors in a Bottom-Gate/Bottom-Contact Cross-Linked Structure.Sensors (Basel, Switzerland) · 2023Article
- Rapid point-of-care detection of SARS-CoV-2 infection in exhaled breath using ion mobility spectrometry: a pilot study.European journal of medical research · 2023Article
- Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication.NPJ science of food · 2023Article
- Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers.Sensors (Basel, Switzerland) · 2023Review
- Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant.JAMA network open · 2023Article
- Paper-Based Multiplex Sensors for the Optical Detection of Plant Stress.Micromachines · 2023Article
- Research progress of electronic nose technology in exhaled breath disease analysis.Microsystems & nanoengineering · 2023Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by utilizing a combination of clinical signs and symptoms, laboratory test, imaging measurement (e.g., chest computed tomography scan), and multivariable clinical prediction models, including the electronic nose. Here, we report on the development and use of a low cost, noninvasive method to rapidly sniff out COVID-19 based on a portable electronic nose (GeNose C19) integrating an array of metal oxide semiconductor gas sensors, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total of 615 breath samples composed of 333 positive and 282 negative samples. The samples were obtained from 43 positive and 40 negative COVID-19 patients, respectively, and confirmed with RT-qPCR at two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis, support vector machine, stacked multilayer perceptron, and deep neural network) were utilized to identify the top-performing pattern recognition methods and to obtain a high system detection accuracy (88-95%), sensitivity (86-94%), and specificity (88-95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.
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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.