The DOI matches its Crossref record; 170 of the 170 references it cites were found in a registry or an index.
Electronic nose: a non-invasive technology for breath analysis of diabetes and lung cancer patients
Abstract
In human exhaled breath, more than 3000 volatile organic compounds (VOCs) are found, which are directly or indirectly related to internal biochemical processes in the body. Electronic noses (E-noses) could play a potential role in screening/analyzing various respiratory and systemic diseases by studying breath signatures. An E-nose integrates a sensor array and an artificial neural network that responds to specific patterns of VOCs, and thus can act as a non-invasive technology for disease monitoring. The gold standard blood glucose monitoring test for diabetes diagnostics is invasive and highly uncomfortable. This contributes to the massive need for technologies which are non-invasive and can be used as an alternative to blood measurements for glucose detection. While lung cancer is one of the deadliest cancers with the highest death rate and an extremely high yearly global burden, the conventional diagnosis means, such as sputum cytology, chest radiography, or computed tomography, do not support wide-range population screening. A few standard non-invasive techniques, such as mass spectrometry and gas chromatography, are expensive, non-portable, and require skilled personnel for operation and are again not suitable for large-scale screening. Breath contains markers for both diabetes and lung cancer along with markers for several diseases and thus, a non-invasive technique such as the E-nose would greatly improve analysis procedures over existing invasive methods. This review shows the state-of-the-art technologies for VOC detection and machine learning approaches for two clinical models: diabetes and lung cancer detection.
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In the citing paper
“VOCs in breath can also be used as biomarkers for the detection and monitoring of diabetes (Behera et al. 2019).”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Behera et al. 2019
In the citing paper
“Changes in the levels of certain VOCs, such as acetone and isoprene, have been associated with diabetes and its complications.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Behera et al. 2019
Citations
Cites
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Cited by
Sorted by citations
Non-Invasive Blood Glucose Monitoring Technology: A Review
Tang, L., Chang, S. J., Chen, C.-J., & Liu, J.-T. (2020) · Sensors · doi:10.3390/s20236925 · Cited by 481
Wang, C., He, T., Zhou, H., Zhang, Z., & Lee, C. (2023) · Bioelectronic Medicine · doi:10.1186/s42234-023-00118-1 · Cited by 295
Chaki, J., Ganesh, S. T., Cidham, S. K., & Theertan, S. A. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.06.013 · Cited by 301
On-Site Detection of Volatile Organic Compounds (VOCs)
Epping, R., & Koch, M. (2023) · Molecules · doi:10.3390/molecules28041598 · Cited by 198
Research progress of electronic nose technology in exhaled breath disease analysis
Li, Y., Wei, X., Zhou, Y., Wang, J., & You, R. (2023) · Microsystems & Nanoengineering · doi:10.1038/s41378-023-00594-0 · Cited by 168
Metadata
- Authors
- Behera, B., Joshi, R., Anil Vishnu, G. K., Bhalerao, S., & Pandya, H. J.
- Affiliations
- Indian Institute of Science Bangalore (IN)
- Corresponding author
- Pandya, Hardik J
- Venue
- Journal of Breath Research
- Publisher
- IOP Publishing
- Published
- 2019-03-06
- Volume
- 13
- Issue
- 2
- Pages
- 024001
- ISSN
- 1752-7155 (ISSN-L)
- 1752-7163
- OpenAlex
- W2910501941
- Cited by
- 179
- References
- 170
- Citations by year
- 2026: 12 · 2025: 21 · 2024: 28 · 2023: 25 · 2022: 35 · 2021: 24 · 2020: 25 · 2019: 9
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Breath Research (publishedVersion)
- PubMed (publishedVersion)
- NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) (acceptedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Chemical Sensor Technologies
- Physical Sciences › Chemistry › Spectroscopy › Spectroscopy and Laser Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- electronic nose, breath analysis, volatile organic compounds, diabetes diagnosis, lung cancer detection, non-invasive diagnosis, blood glucose monitoring, VOC detection, machine learning classification, disease screening
- MeSH terms
- Machine Learning, Breath Tests, Diabetes Mellitus, Humans, Lung Neoplasms, Nanoparticles, Electronic Nose
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