Non-invasive Blood Glucose Monitoring Research Articles

Advanced biomaterials for diabetes healthcare and complication therapy: A review

With the rapid increase in the number of patients with diabetes, exploring more effective and convenient methods to lower blood sugar levels is becoming increasingly important. Biomaterials are a potential solution in this field, owing to their unique customizability and biocompatibility. These materials can be used in smart drug delivery systems to achieve precise control of insulin release, or as islet cell encapsulation materials to achieve effective transplantation of islet cells. Among these, new responsive biomaterials can automatically adjust the release of insulin according to real-time changes in blood sugar levels, thereby enabling personalized and automated treatment. In addition, biomaterials are used to develop noninvasive blood glucose monitoring technologies to further simplify diabetes management. Although these applications are still in the research or early pilot stage, their potential to improve diabetes treatment and the quality of life of patients is already evident. In this Review, we discuss the current progress, limitations, and potential of biomaterials for the treatment of diabetes and its complications.

Deep Learning‐Coupled Metabolic Heat Integrated Sensing System for Noninvasive Continuous Monitoring of Blood Glucose

The dynamic and continuous monitoring of blood glucose (BG) concentration is crucial for the health management of diabetic patients. Despite its importance, significant challenges remain in the development of effective BG monitoring technologies. Metabolic heat conformation (MHC) offers a promising solution due to its noninvasiveness and reliability. However, progress in MHC technology is hindered by the complexities of multisensor integration and the intricate correlation between MHC and BG. Herein, a wearable BG continuous monitoring device based on metabolic heat integrated sensing (MHIS) is developed, combined with a deep learning network to enable continuous BG detection using MHC principles. The MHIS device integrates a miniaturized circuit and intelligent secondary signal processing, allowing for the simultaneous acquisition and integrated operation of various physiological parameters, including metabolic heat production. By integrating a gate recurrent unit neural network, a model is established to facilitate continuous BG monitoring. The wearable device has a certain accuracy, and when analyzed in comparison with commercial noninvasive glucose meters, the mean absolute relative error meets international standards. The deep learning‐enhanced MHIS system proposed in this work enables noninvasive BG monitoring, paving the way for advancements in personalized healthcare management and offering new opportunities in digital healthcare consultation.

Clinical Validation of a Prototype Smart Non-Invasive Pregnancy Glucose Monitor

The aim of this study is to evaluate the effectiveness of a smart non-invasive blood glucose monitor prototype during pregnancy through clinical validation. The monitor utilizes near-infrared spectroscopy combined with AI big data analysis of photoelectric volumetric pulse wave data to achieve non-invasive monitoring of blood glucose in women during pregnancy. The research team developed a monitor that employs a sensing chip, effectively overcoming the problems of weak signals and individual differences in non-invasive blood glucose monitoring. The user experience is enhanced by visualizing the test results on the accompanying cell phone APP (application) of the smart non-invasive pregnancy blood glucose monitor. Clinical validation revealed that the non-invasive monitoring data for pregnant women aged 20~30 years significantly differed from those obtained via traditional blood glucose measurement methods, whereas no significant difference ( P<0.05) was observed for pregnant women aged 31~42 years. The study concluded that further calibration of the monitor and an expansion of the sample size are necessary to enhance consistency with invasive glucose monitoring results.

Sensitivity Approach for Blood Glucose Impedance Data Using the Geselowitz Theorem

The article presents a preclinical study focused on developing a non-invasive blood glucose monitoring device. Current glucose monitoring methods, such as capillary or venous tests, are inconvenient for frequent use, making continuous monitoring challenging. This study explores the BGP (Bruna Gabriela Pedro) model, which links electrical impedance to blood glucose levels, as a potential alternative. Using the Geselowitz Theorem and impedance spectroscopy in a 4-electrode configuration, the study investigates the relationship between blood conductivity and glucose concentration. Impedance spectra were recorded for five human blood samples with glucose concentrations between 106 and 188 mg/dL. These samples were housed in an insulating container with four stainless steel electrodes. Conductivity was calculated using the Geselowitz Theorem at 1.0, 3.9, and 24.9 kHz. A finite element model built in COMSOL was employed to assess electrode sensitivity, which had a maximum regression error of 3.75 %. The results demonstrated a sigmoid relationship between blood glucose concentration and conductivity, with impedance decreasing non-linearly as glucose levels increased. Sensitivity diminished for glucose concentrations exceeding 188 mg/dL. An increase of 82 mg/dL in glucose concentration led to conductivity changes of 1.14, 2.82, and 5.14 S/m at frequencies of 1.0, 3.9, and 24.9 kHz, respectively. The findings suggest that the BGP analytical model could be refined through the inclusion of additional data on glucose's impact on electrical impedance. These improvements could support the development of non-invasive glucose meters. The research holds significant clinical potential for the creation of a simple, cost-effective glucose monitoring device. Continuous glucose monitoring could enhance diagnostic accuracy and support better therapeutic decision-making, particularly in emergency care. The affordability and accessibility of such a device may benefit a broad patient base.

The Self-Powered Thermoelectric System Driving an Electric Field for Raman-Spectrum Glucose Quantitative Analysis

For a long time, blood glucose monitoring has been a challenging issue in the field of medicine. Although it is possible to visit a hospital for regular blood tests to track blood sugar concentration, the physiological reactions triggered by blood collection might lead to feeling weak, dizzy, or even vomiting. Therefore, especially for diabetic patients, they need to frequently monitor their blood sugar concentration in their daily lives to ensure their health. Currently, the primary methods for blood glucose testing still rely on invasive techniques, such as using fingertip glucose meters and other methods which require skin puncture to obtain blood samples. Although these methods provide a quick assessment of current blood glucose concentration, they come with the pain and discomfort of blood collection process and the potential risk of infection. As a result, in recent years, many researchers have been dedicated to developing non-invasive blood glucose monitoring methods. However, accurately measuring glucose concentration inside the human body remains a challenging task. One of the primary issues is the presence of strong noise and background noise. To avoid this challenge, we have developed an innovative detection platform that is combines surface-enhanced Raman spectroscopy (SERS) and external electric field sensing technology. With the electromagnetic effect, we add external electric field in our platform to excite more the surface plasmon resonance. By using this method, we can achieve signal amplification to detect lower concentrations of glucose. Now, we can detect down to 0.001mM of glucose. We also exhibit self-powered electric field system, using the flexible themoelctric device to combine our platform. By the property of self-powered, we success to create a self-powered electric field system. We focus on driving themoelectric through the temperature different between body temperature and environment. The approach is aimed at generating power from the human body, providing a self-powered thermoelectric system for Raman-spectrum glucose sensors. This innovative approach holds promise for advancing non-invasive blood glucose monitoring and benefiting a larger population of diabetes patients in the future.

A Glycemic Status Classification Model Using a Radiofrequency Noninvasive Blood Glucose Monitor.

Despite significant efforts in the development of noninvasive blood glucose (BG) monitoring solutions, delivering an accurate, real-time BG measurement remains challenging. We sought to address this by using a novel radiofrequency (RF) glucose sensor to noninvasively classify glycemic status. The study included 31 participants aged 18-65 with prediabetes or type 2 diabetes and no other significant medical history. During control sessions and oral glucose tolerance test sessions, data were collected from both a RF sensor that rapidly scans thousands of frequencies and concurrently from a venous blood draw measured with an US Food and Drug Administration (FDA)-cleared glucose hospital meter system to create paired observations. We trained a time series forest machine learning model on 80% of the paired observations and reported results from applying the model to the remaining 20%. Our findings show that the model correctly classified glycemic status 93.37% of the time as high, normal, or low.

A wearable non-enzymatic sensor for continuous monitoring of glucose in human sweat

To enhance personalized diabetes management, there is a critical need for non-invasive wearable electrochemical sensors made from flexible materials to enable continuous monitoring of sweat glucose levels. The main challenge lies in developing glucose sensors with superior electrochemical characteristics and high adaptability. Herein, we present a wearable sensor for non-enzymatic electrochemical glucose analysis. The sensor was synthesized using hydrothermal and one-pot preparation methods, incorporating gold nanoparticles (AuNPs) functionalized onto aminated multi-walled carbon nanotubes (AMWCNTs) as an efficient catalyst, and crosslinked with carboxylated styrene butadiene rubber (XSBR) and PEDOT:PSS. The sensors were then integrated onto screen-printed electrodes (SPEs) to create flexible glucose sensors (XSBR-PEDOT:PSS-AMWCNTs/AuNPs/SPE). Operating under neutral conditions, the sensor exhibits a linear range of 50 μmol/L to 600 μmol/L, with a limit of detection limit of 3.2 μmol/L (S/N = 3), enabling the detection of minute glucose concentrations. The flexible glucose sensor maintains functionality after 500 repetitions of bending at a 180° angle, without significant degradation in performance. Furthermore, the sensor exhibits exceptional stability, repeatability, and resistance to interference. Importantly, we successfully monitored changes in sweat glucose levels by applying screen-printed electrodes to human skin, with results consistent with normal physiological blood glucose fluctuations. This study details the fabrication of a wearable sensor characterized by ease of manufacture, remarkable flexibility, high sensitivity, and adaptability for non-invasive blood glucose monitoring through non-enzymatic electrochemical analysis. Thus, this streamlined fabrication process presents a novel approach for non-invasive, real-time blood glucose level monitoring.

Development of a passive wireless sensor for fluidic detection and characterization utilizing the PCB-based coplanar electrode (PCE) configuration

During the global economic development, there's a growing focus on healthcare, especially in the advancement of medical diagnostic technologies, with a significant emphasis on glucose level evaluation. Glucose biosensors, predominantly electrochemical, have evolved over four generations, with the first three being enzyme-based and known for sensitivity and cost-effectiveness, albeit with limitations due to environmental susceptibility and reliance on enzyme activity. Recent advancements in non-invasive blood glucose monitoring, utilizing optical, microwave, and electrochemical techniques, offer diverse benefits without tissue penetration. Among these, impedance sensing stands out due to its flexibility and integration capability in handheld devices. This study proposes a wireless passive impedance method leveraging the inductor-capacitor (LC) sensing technique and PCB (Printed Circuit Board)-based coplanar electrode (PCE) configuration for fluidic sample detection. The proposed system integrates a two-coplanar-electrode layout with a square spiral inductor to assess fluidic conductivity and characterize various fluid types within samples. The effectiveness of this configuration was validated through experiments with NaCl and glucose solutions, confirming the feasibility of integrating PCB-based coplanar electrodes into conventional LC passive wireless sensing designs for fluidic detection and characterization.

Comparison of Ensemble Method Performance in Classifying Blood Sugar Levels Output from Non-Invasive Device

Diabetes Mellitus (DM) is a persistent health issue in many countries and is a leading cause of heart disease, kidney failure, and blindness The International Diabetes Federation (IDF) estimated in 2019 that at least 463 million people worldwide aged 20-79 suffer from diabetes. This number is expected to rise to 578 million by 2030 and 700 million by 2045. Machine learning is a type of machine learning that is very helpful in various fields, including healthcare. In classification cases, ensemble methods classify by combining decisions from several other models, one way being through majority voting. Ensemble methods often produce more accurate classification or prediction results. Several ensemble methods include random forest, extra trees, rotation forest, and double random forest. The data used in this study is part of research on the development and clinical testing of a prototype non-invasive blood glucose monitoring device by the non-invasive biomarking team at IPB. The data includes both invasive and non-invasive blood glucose measurements collected in 2019. This study compares the performance of the random forest, extra trees, rotation forest, and double random forest models on blood glucose level data obtained from non-invasive devices. The research results show that the Rotation Forest algorithm is the best model, with the highest average accuracy compared to the other three algorithms, achieving an accuracy level of 0.7142857 (71.42%).

Optimizing the Performance of AI Model for Non-Invasive Continuous Glucose Monitoring: Hyperparameter Tuning and Random Oversampling Approach

Diabetes Mellitus (DM) as a non-communicable disease (NCD) continues to increase every year. Continuous glucose monitoring (CGM) is essential for effective DM management. However, existing disposable glucose monitoring methods still rely on invasive techniques, cause pain, and lack continuous monitoring capabilities. On the other hand, non-invasive techniques are not feasible for CGM due to the biometric data's complexity and the classification system's inadequate performance. This study aims to develop a non-invasive technology to improve the performance of a non-invasive blood glucose classification system using Artificial Intelligence (AI), specifically Convolutional Neural Network (CNN) and an oversampling technique. The oversampling technique could improve data quantity by balancing the amount of data for each class. This study recruited twenty-three participants in the age range of 20 to 22 years comprising seven females and fifteen males. During data recording sessions, blood glucose levels were simultaneously assessed using a gold-standard glucometer and a non-invasive CGM prototype. The proposed CNN model successfully improved the classification accuracy of non-invasive blood glucose monitoring significantly. With the implementation of oversampling for augmenting the data, the accuracy of the proposed model increased to more than 88%. This study concludes that non-invasive approaches combined with AI technology have the potential to provide a convenient and pain-free alternative to traditional monitoring methods, significantly improving diabetes management and enhancing the overall quality of life for those affected by this condition. These findings could revolutionize the field of diabetes management, offering a more comfortable and accurate monitoring solution that could potentially transform the lives of millions of diabetes patients.

Design and Development of a Non-invasive Opto-Electronic Sensor for Blood Glucose Monitoring Using a Visible Light Source.

Background The management of diabetes is critically dependent on the continuous monitoring of blood glucose levels. Contemporary approaches primarily utilize invasive methods, which often prove to be uncomfortable and can deter patient adherence. There is a pressing need for the development of novel strategies that improve patient compliance and simplify the process of glucose monitoring. Aim and objectives The primary objective of this research is to develop a non-invasive blood glucose monitoring system (NIBGMS) that offers a convenient alternative to conventional invasive methods. This study aims to demonstrate the feasibility and accuracy of using visible laser light at a wavelength of 650 nm for glucose monitoring and to address physiological and technical challenges associated with in vivo measurements. Methods Our approach involved the design of a device that exploits the quantitative relationship between glucose concentration and the refraction phenomena of laser light. The system was initially calibrated and tested using glucose solutions across a range of concentrations (25-500 mg/dL). To get around the problems that come up when people's skin and bodies are different, we combined an infrared (IR) transmitter (800 nm) and receiver that checks for changes in voltage, which are indicative of glucose levels. Results The prototype device was compared with a commercially available blood glucose monitor (Accu-Chek active machine; Roche Diabetes Care, Inc., Mumbai, India). The results demonstrated an average linearity of 95.7% relative to the Accu-Chek machine, indicating a high level of accuracy in the non-invasive measurement of glucose levels. Conclusions The findings suggest that our NIBGMS holds significant promise for clinical application. It reduces the discomfort associated with blood sampling and provides reliable measurements that are comparable to those of existing invasive methods. The successful development of this device paves the way for further commercial translation and could significantly improve the quality of life for individuals with diabetes, by facilitating easier and more frequent monitoring.

Noninvasive Real-Time Glucose Monitoring Is in the Near Future.

Objective: Since the introduction of continuous glucose monitoring (CGM) technology, developers have rigorously researched the feasibility of creating a noninvasive glucose monitoring device. In a recent pilot study, investigators reported a strong correlation between glucose values obtained from novel noninvasive monitoring device (GWave) values to venous and capillary glucose measurements. Research Design and Methods: We investigated whether the level of accuracy observed in the pilot study could be reproduced in a larger cohort, using a smaller third-generation manufacturable device (Gen III GWave) containing a standardized sensor chip that can be mass produced for commercial use. The evaluation assessed concordance with capillary blood glucose, reproducibility between two Gen III devices, and accuracy during insulin-induced hypoglycemia. Results: Assessment of samples from 75 subjects (type 2 diabetes, n = 6; type 1 diabetes, n = 28; nondiabetic pregnant subjects, n = 10; and nondiabetic, n = 31) showed that 97% of values were in Zone A with 3% in Zone B of the Clarke Error Grid, with a mean absolute relative difference of 6.7% from reference blood glucose. Comparison between two independent Gen III GWave devices demonstrated reproducibility between the sensors (R2 = 0.95), with 100% of values within Zone A. In the hypoglycemia assessment, measurements from the Gen III sensor tightly followed the capillary glucose measurements down to 42 mg/dL (2.3 mmol/L), whereas the CGM measurements from two different CGM only converged with the GWave and capillary glucose readings after 90 min of decreasing glucose levels. Conclusion: Our results show promise as potentially the first noninvasive technology. Future studies will focus on larger number of people in all glucose ranges. Real-time noninvasive blood glucose monitoring is possible using GWave technology.

Development of Sweat and Saliva Glucose Sensors as Alternative for Non-Invasive Blood Glucose Monitoring

Development of Sweat and Saliva Glucose Sensors as Alternative for Non-Invasive Blood Glucose Monitoring

The progress of clinical research on the detection of 1,5-anhydroglucitol in diabetes and its complications.

1,5-Anhydroglucitol (1,5-AG) is sensitive to short-term glucose fluctuations and postprandial hyperglycemia, which has great potential in the clinical application of diabetes as a nontraditional blood glucose monitoring indicator. A large number of studies have found that 1,5-AG can be used to screen for diabetes, manage diabetes, and predict the perils of diabetes complications (diabetic nephropathy, diabetic cardiovascular disease, diabetic retinopathy, diabetic pregnancy complications, diabetic peripheral neuropathy, etc.). Additionally, 1,5-AG and β cells are also associated with each other. As a noninvasive blood glucose monitoring indicator, salivary 1,5-AG has much more benefit for clinical application; however, it cannot be ignored that its detection methods are not perfect. Thus, a considerable stack of research is still needed to establish an accurate and simple enzyme assay for the detection of salivary 1,5-AG. More clinical studies will also be required in the future to confirm the normal reference range of 1,5-AG and its role in diabetes complications to further enhance the blood glucose monitoring system for diabetes.

Non-invasive Blood Glucose Monitoring System

The project presents a novel approach to blood glucose monitoring, offering a non-invasive and pain-free method for individuals with diabetes to track their glucose levels. The system utilizes advanced sensor technology coupled with signal processing techniques to detect glucose levels in the body without the need for traditional needle pricking. The key components of the system include a power supply, voltage regulator, ESP32 microcontroller, LCD display, photo sensor, amplifier circuit, and linearization circuit, all seamlessly integrated to provide accurate and real-time glucose readings. This breakthrough technology holds great promise in revolutionizing diabetes management, enhancing the quality of life for millions of affected individuals.By placing the sensor on the individual’s hand, the Glucometer system employs a combination of photonic and electronic principles to non-invasively measure glucose levels. The photo sensor captures specific wavelengths of light that interact with glucose molecules within the tissue, generating a signal proportional to the glucose concentration. This signal is then amplified and processed through the integrated circuits, ensuring accurate linearization and calibration. The ESP32 microcontroller orchestrates these operations, facilitating seamless communication with the LCD display to present the glucose reading in a clear and user-friendly format.The Glucometer’s innovative design eliminates the discomfort associated with traditional blood sampling methods, making it a highly accessible and convenient tool for diabetes management. The system’s efficiency in providing accurate glucose readings without the need for invasive procedures represents a significant advancement in healthcare technology. With further development and refinement, the Glucometer holds the potential to significantly enhance the daily lives of individuals living with diabetes, offering a reliable and painless means of monitoring blood glucose levels.

NON-INVASIVE BLOOD GLUCOSE AND THYROID MONITORING SYSTEM

Frequent blood tests have historically been necessary to monitor thyroid function and blood glucose levels, which can be painful and inconvenient for patients. In this work, we provide a unique non-invasive blood glucose and thyroid monitoring device. With the use of cutting-edge sensor technology, our system can precisely measure thyroid and glucose via the skin without the need for blood draws or needles. This method provides a simple and easy substitute for conventional techniques, which may increase patient adherence to monitoring schedules. This non-invasive monitoring system's creation marks a substantial leap forward in medical science and could improve the way thyroid and diabetic diseases are managed. Additional investigation and clinical studies are necessary to confirm the efficacy and dependability of this novel technology in real world. Key Words: Non-invasive, Blood glucose, Thyroid, Monitoring system, Sensor technology, Healthcare innovation

AI-based non-invasive blood glucose monitoring: A Scoping Review (Preprint)

AI-based non-invasive blood glucose monitoring: A Scoping Review (Preprint)

Cost-Effective Non-Invasive Blood Glucose Monitoring System with Mobile Application for Management of Diabetic Patients

Cost-Effective Non-Invasive Blood Glucose Monitoring System with Mobile Application for Management of Diabetic Patients

A non-invasive smart healthcare monitoring system based on the Internet of things

&lt;p&gt;A worldwide increase in healthcare problems and disorders underscored the significance of across the globe healthcare monitoring (HCM). Several countries have increased lifespans due to technological medical developments, government health initiatives, and personal cleanliness. Globalization has led to a gradually rising elderly population and a decrease in fertility, which might result in problems with socioeconomic status. In order to assist older adults, HCM technologies must be cost-effective and simple to implement. Autonomous HCM could be feasible, incorporating wearable devices with sensors, actuators, and communication. The above approach enables practical and efficient personal medical care for older people, minimizing their requirement for costly hospital treatment. The entire study emphasizes the non-invasive blood glucose monitoring (NIBGM). The idea for the project includes developing a non-invasive medical device with RFID tags login, data storage, and smart sensors that allow the monitoring of several organs. The small in size, interconnected sensor monitors body temperature (BT), blood glucose level (BGL), blood pressure (BP), heart rate (HR), and oxygen saturation (OS). The internet-based monitoring device permits healthcare providers to keep track of patient’s health metrics in real-time and send data to remote places. The new method allows healthcare professionals to present rapid assistance and reinforcement, enhancing the results for patients.&lt;/p&gt;

Blood glucose monitoring devices for type 1 diabetes: a journey from the food and drug administration approval to market availability.

Blood glucose monitoring constitutes a pivotal element in the clinical management of Type 1 diabetes (T1D), a globally escalating metabolic disorder. Continuous glucose monitoring (CGM) devices have demonstrated efficacy in optimizing glycemic control, mitigating adverse health outcomes, and augmenting the overall quality of life for individuals afflicted with T1D. Recent progress in the field encompasses the refinement of electrochemical sensors, which enhances the effectiveness of blood glucose monitoring. This progress empowers patients to assume greater control over their health, alleviating the burdens associated with their condition, and contributing to the overall alleviation of the healthcare system. The introduction of novel medical devices, whether derived from existing prototypes or originating as innovative creations, necessitates adherence to a rigorous approval process regulated by the Food and Drug Administration (FDA). Diverse device classifications, stratified by their associated risks, dictate distinct approval pathways, each characterized by varying timelines. This review underscores recent advancements in blood glucose monitoring devices primarily based on electrochemical sensors and elucidates their regulatory journey towards FDA approval. The advent of innovative, non-invasive blood glucose monitoring devices holds promise for maintaining stringent glycemic control, thereby preventing T1D-associated comorbidities, and extending the life expectancy of affected individuals.