In-human testing of a non-invasive continuous low-energy microwave glucose sensor with advanced machine learning capabilities
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F23%3A50020824" target="_blank" >RIV/62690094:18470/23:50020824 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.bios.2023.115668" target="_blank" >https://doi.org/10.1016/j.bios.2023.115668</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.bios.2023.115668" target="_blank" >10.1016/j.bios.2023.115668</a>
Alternative languages
Result language
angličtina
Original language name
In-human testing of a non-invasive continuous low-energy microwave glucose sensor with advanced machine learning capabilities
Original language description
Continuous glucose monitoring schemes that avoid finger pricking are of utmost importance to enhance the comfort and lifestyle of diabetic patients. To this aim, we propose a microwave planar sensing platform as a potent sensing technology that extends its applications to biomedical analytes. In this paper, a compact planar resonator-based sensor is introduced for noncontact sensing of glucose. Furthermore, in vivo and in-vitro tests using a microfluidic channel system and in clinical trial settings demonstrate its reliable operation. The proposed sensor offers real-time response and a high linear correlation (R-2 similar to 0.913) between the measured sensor response and the blood glucose level (GL). The sensor is also enhanced with machine learning to predict the variation of body glucose levels for non-diabetic and diabetic patients. This addition is instrumental in triggering preemptive measures in cases of unusual glucose level trends. In addition, it allows for the detection of common artifacts of the sensor as anomalies so that they can be removed from the measured data. The proposed system is designed to noninvasively monitor interstitial glucose levels in humans, introducing the opportunity to create a customized wearable apparatus with the ability to learn.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
BIOSENSORS & BIOELECTRONICS
ISSN
0956-5663
e-ISSN
1873-4235
Volume of the periodical
241
Issue of the periodical within the volume
December
Country of publishing house
GB - UNITED KINGDOM
Number of pages
14
Pages from-to
"Article Number: 115668"
UT code for WoS article
001085395000001
EID of the result in the Scopus database
2-s2.0-85172221224