Non-invasively accuracy enhanced blood glucose sensor using shallow dense neural networks with NIR monitoring and medical features

Abstract

Non-invasive and accurate method for continuous blood glucose monitoring, the self-testing of blood glucose is in quest for better diagnosis, control and the management of diabetes mellitus (DM). Therefore, this study reports a multiple photonic band near-infrared (mbNIR) sensor augmented with personalized medical features (PMF) in Shallow Dense Neural Networks (SDNN) for the precise, inexpensive and pain free blood glucose determination. Datasets collected from 401 blood samples were randomized and trained with ten-fold validation. Additionally, a cohort of 234 individuals not included in the model training set were investigated to evaluate the performance of the model. The model achieved the accuracy of 97.8% along with 96.0% precision, 94.8% sensitivity and 98.7% specificity for DM classification based on a diagnosis threshold of 126 mg/dL for diabetes in fasting blood glucose. For non-invasive real-time blood glucose monitoring, the model exhibited ± 15% error with 95% confidence interval and the detection limit of 60–400 mg/dL, as validated with the standard hexokinase enzymatic method for glucose estimation. In conclusion, this proposed mbNIR based SDNN model with PMF is highly accurate and computationally cheaper compared to similar previous works using complex neural network. Some groups proposed using complicated mixed types of sensors to improve noninvasive glucose prediction accuracy; however, the accuracy gain over the complexity and costs of the systems harvested is still in questioned (Geng et al. in Sci Rep 7:12650, 2017). None of previous works report on accuracy enhancement of NIR/NN using PMF. Therefore, the proposed SDNN over PMF/mbNIR is an extremely promising candidate for the non-invasive real-time blood glucose monitoring with less complexity and pain-free.

Electrospun Manganese-Based Metal–Organic Frameworks for MnOx Nanostructures Embedded in Carbon Nanofibers as a High-Performance Nonenzymatic Glucose Sensor

“…Many noninvasive technologies are currently in development in a variety of industries. 8,9 Reverse iontophoresis, 10 absorbance spectrosco-py, 11 and near-infrared spectroscopy 12 are a few examples. However, none of them have yet attained the sensitivity and precision required to completely replace finger-prick monitoring.…”

Machine and Deep Learning Dominate Recent Innovations in Sensors, Signals and Imaging Informatics

“…The SSII field encompasses a vast research area with myriad potential clinical applications, and according to our annual reviews of the field, thousands of articles are published each year [2][3][4]. A growing number of research and review papers have been identified from year to year, with a large majority applying machine or deep paper from the sensor informatics subfield reports on a very innovative multiple photonic band nearinfrared (mbNIR) sensor augmented with personalized medical features (PMF) in Shallow Dense Neural Networks (SDNN) for accurate, low-cost, and painless determination of the blood glucose level [7].…”

Flexible Specific Determination of Glucose in Solution, Blood Serum, and Sweat Using a Terahertz Hydrogel‐Functionalized Metamaterial

“…[40,41] This system is capable of penetrating the human body surface without causing ionizing radiation damage, offering the potential to achieve continuous detection of glucose concentration in vitro and improve the management of diabetes. [42][43][44][45] However, for biological solutions, the large amount of moisture in the solution leads to the strong absorption of terahertz waves, making direct detection of biological solutions via transmission THz-TDS systems ineffective. [46,47] To address this, there is an urgent need to develop a terahertz sensor that is extremely sensitive to minute changes in hydration state, aiming to balance the absorption of terahertz waves by water and shorten the swelling equilibrium response time, thereby improving detection efficiency of glucose biomolecules.…”