In-human testing of a non-invasive continuous low-energy microwave glucose sensor with advanced machine learning capabilities

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

In-human testing of a non-invasive continuous low-energy microwave glucose sensor with advanced machine learning capabilities

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

In-human testing of a non-invasive continuous low-energy microwave glucose sensor with advanced machine learning capabilities

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

BIOSENSORS &amp; BIOELECTRONICS

ISSN

0956-5663

e-ISSN

1873-4235

Svazek periodika

241

Číslo periodika v rámci svazku

December

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

14

Strana od-do

"Article Number: 115668"

Kód UT WoS článku

001085395000001

EID výsledku v databázi Scopus

2-s2.0-85172221224