Dataset Information

Sampling of fluid through skin with magnetohydrodynamics for noninvasive glucose monitoring.

ABSTRACT: Out of 463 million people currently with diabetes, 232 million remain undiagnosed. Diabetes is a threat to human health, which could be mitigated via continuous self-monitoring of glucose. In addition to blood, interstitial fluid is considered to be a representative sample for glucose monitoring, which makes it highly attractive for wearable on-body sensing. However, new technologies are needed for efficient and noninvasive sampling of interstitial fluid through the skin. In this report, we introduce the use of Lorentz force and magnetohydrodynamics to noninvasively extract dermal interstitial fluid. Using porcine skin as an ex-vivo model, we demonstrate that the extraction rate of magnetohydrodynamics is superior to that of reverse iontophoresis. This work seeks to provide a safe, effective, and noninvasive sampling method to unlock the potential of wearable sensors in needle-free continuous glucose monitoring devices that can benefit people living with diabetes.

SUBMITTER: Hakala TA

PROVIDER: S-EPMC8027418 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Sampling of fluid through skin with magnetohydrodynamics for noninvasive glucose monitoring.

Hakala Tuuli A TA García Pérez Alejandro A Wardale Melissa M Ruuth Ida A IA Vänskä Risto T RT Nurminen Teemu A TA Kemp Emily E Boeva Zhanna A ZA Alakoskela Juha-Matti JM Pettersson-Fernholm Kim K Hæggström Edward E Bobacka Johan J

Scientific reports 20210407 1

Out of 463 million people currently with diabetes, 232 million remain undiagnosed. Diabetes is a threat to human health, which could be mitigated via continuous self-monitoring of glucose. In addition to blood, interstitial fluid is considered to be a representative sample for glucose monitoring, which makes it highly attractive for wearable on-body sensing. However, new technologies are needed for efficient and noninvasive sampling of interstitial fluid through the skin. In this report, we intr ...[more]

PMID: 33828144

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Project description:BackgroundCurrent blood glucose monitoring (BGM) methods are often invasive and require repetitive pricking of a finger to obtain blood samples, predisposing individuals to pain, discomfort, and infection. Noninvasive blood glucose monitoring (NIBGM) is ideal for minimizing discomfort, reducing the risk of infection, and increasing convenience.ObjectiveThis review aimed to map the use cases of artificial intelligence (AI) in NIBGM.MethodsA systematic scoping review was conducted according to the Arksey O'Malley five-step framework. Eight electronic databases (CINAHL, Embase, PubMed, Web of Science, Scopus, The Cochrane-Central Library, ACM Digital Library, and IEEE Xplore) were searched from inception until February 8, 2023. Study selection was conducted by 2 independent reviewers, descriptive analysis was conducted, and findings were presented narratively. Study characteristics (author, country, type of publication, study design, population characteristics, mean age, types of noninvasive techniques used, and application, as well as characteristics of the BGM systems) were extracted independently and cross-checked by 2 investigators. Methodological quality appraisal was conducted using the Checklist for assessment of medical AI.ResultsA total of 33 papers were included, representing studies from Asia, the United States, Europe, the Middle East, and Africa published between 2005 and 2023. Most studies used optical techniques (n=19, 58%) to estimate blood glucose levels (n=27, 82%). Others used electrochemical sensors (n=4), imaging (n=2), mixed techniques (n=2), and tissue impedance (n=1). Accuracy ranged from 35.56% to 94.23% and Clarke error grid (A+B) ranged from 86.91% to 100%. The most popular machine learning algorithm used was random forest (n=10) and the most popular deep learning model was the artificial neural network (n=6). The mean overall checklist for assessment of medical AI score on the included papers was 33.5 (SD 3.09), suggesting an average of medium quality. The studies reviewed demonstrate that some AI techniques can accurately predict glucose levels from noninvasive sources while enhancing comfort and ease of use for patients. However, the overall range of accuracy was wide due to the heterogeneity of models and input data.ConclusionsEfforts are needed to standardize and regulate the use of AI technologies in BGM, as well as develop consensus guidelines and protocols to ensure the quality and safety of AI-assisted monitoring systems. The use of AI for NIBGM is a promising area of research that has the potential to revolutionize diabetes management.

Dataset Information

Sampling of fluid through skin with magnetohydrodynamics for noninvasive glucose monitoring.

Publications

Sampling of fluid through skin with magnetohydrodynamics for noninvasive glucose monitoring.

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