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Review of Non-Invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone
Open accessAbstract
Annual deaths in the U.S. attributed to diabetes are expected to increase from 280,210 in 2015 to 385,840 in 2030. The increase in the number of people affected by diabetes has made it one of the major public health challenges around the world. Better management of diabetes has the potential to decrease yearly medical costs and deaths associated with the disease. Non-invasive methods are in high demand to take the place of the traditional finger prick method as they can facilitate continuous glucose monitoring. Research groups have been trying for decades to develop functional commercial non-invasive glucose measurement devices. The challenges associated with non-invasive glucose monitoring are the many factors that contribute to inaccurate readings. We identify and address the experimental and physiological challenges and provide recommendations to pave the way for a systematic pathway to a solution. We have reviewed and categorized non-invasive glucose measurement methods based on: (1) the intrinsic properties of glucose, (2) blood/tissue properties and (3) breath acetone analysis. This approach highlights potential critical commonalities among the challenges that act as barriers to future progress. The focus here is on the pertinent physiological aspects, remaining challenges, recent advancements and the sensors that have reached acceptable clinical accuracy.
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“The major problem with non‐invasive optical sensors is the specific measurement of glucose with sufficient analytical quality 109.”
From Glucose biosensors in clinical practice: principles, limits and perspectives of currently used devices (2022) · cites this paper as [109]
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3 of the 233 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
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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
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“The major problem with non‐invasive optical sensors is the specific measurement of glucose with sufficient analytical quality [109].”
BackgroundGlucose Detection Devices and Methods Based on Metal–Organic Frameworks and Related Materials
Adeel, M., Asif, K., Rahman, M. M., Daniele, S., Canzonieri, V., & Rizzolio, F. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202106023 · Cited by 182
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Metadata
- Authors
- Shokrekhodaei, M., & Quinones, S.
- Affiliations
- The University of Texas at El Paso (US)
- Department of Metallurgical, Materials and Biomedical Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA
- Corresponding author
- Shokrekhodaei, Maryamsadat
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-02-25
- Volume
- 20
- Issue
- 5
- Pages
- 1251
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7085605
- OpenAlex
- W3007868482
- Cited by
- 221
- References
- 233
- Citations by year
- 2026: 23 · 2025: 31 · 2024: 36 · 2023: 38 · 2022: 38 · 2021: 41 · 2020: 14
- FWCI
- 5.03
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Texas at El Paso
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- non-invasive glucose monitoring, diabetes mellitus, continuous glucose monitoring, optical glucose sensor, clinical accuracy
- MeSH terms
- Acetone, Blood Glucose, Breath Tests, Electricity, Humans, Biosensing Techniques, Optics and Photonics
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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