ArticleACS measurement science au2024
E.Co.Tech Breathalyzer: A Pilot Study of a Non-invasive COVID-19 Diagnostic Tool for Light and Non-smokers.
Article in ACS measurement science au, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Overview in Machine-Learning-Assisted Sensing Techniques for Monitoring COVID-19.Micromachines · 2026Review
- Harnessing Surface-Enhanced Raman Spectroscopy for Breath-Based Diagnostics.Analytical chemistry · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Analysis of exhaled breath offers a noninvasive approach to understanding the metabolic state of the body. This study focuses on the efficacy of an innovative Electrochemical Hand-held Breathalyzer COVID-19 Sensing Technology (E.Co.Tech) for predicting COVID-19 infection, specifically in populations of never or former light smokers. The electrochemical nose technology used in this device aims to discriminate changes in exhaled nitric oxide levels, which are associated with COVID-19-linked respiratory inflammation. The methodology combines the device with a machine learning-based algorithm trained on a diverse data set of breath profiles from both infected and noninfected individuals. A cohort of 46 participants, consisting of never or former light smokers, was recruited. Each participant was tested using the E.Co.Tech prototype device and an iHealth COVID-19 antigen rapid test. The performance of the device was assessed by calculating sensitivity, specificity, positive predictive value, and negative predictive value (NPV). The results demonstrated high specificity (91.11%) and NPV (97.62%) for the device in this demographic group. This case study underscores the potential of E.Co.Tech as a valuable tool for point-of-care COVID-19 diagnosis, particularly in populations with unique smoking histories. The technology's high sensitivity and specificity, along with its rapid results, make it a promising candidate for deployment in resource-limited settings and situations where timely detection is crucial for effective public health management. Further large-scale clinical trials and real-world validations are necessary to establish the device's utility across diverse population groups.
Identifiers
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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.