Ameer B. Alsultani
59348667200
Publications - 5
Advances in invasive and non-invasive glucose monitoring: A review of microwave-based sensors
Publication Name: Sensors and Actuators Reports
Publication Date: 2025-06-01
Volume: 9
Issue: Unknown
Page Range: Unknown
Description:
Effective and continuous glucose monitoring is critical in managing diabetes, which remains a global health challenge affecting millions. Traditional invasive glucose monitoring methods, although accurate, cause discomfort and are unsuitable for frequent measurements necessary for optimal diabetes management. To overcome these limitations, microwave-based sensors have emerged as promising alternatives, providing both invasive and non-invasive monitoring capabilities. This review critically evaluates recent advancements in microwave-based glucose sensors, emphasizing their design methodologies, sensitivity, accuracy, and clinical applicability. By leveraging unique dielectric properties of blood and tissues affected by glucose concentrations, microwave sensors enable precise and potentially pain-free glucose measurements. Despite significant progress, existing sensor technologies face challenges including limited sensitivity ranges, interference from biological tissues, and practical considerations for clinical adoption. This paper aims to guide researchers and healthcare providers by highlighting recent technological innovations, addressing current limitations, and suggesting directions for future research to advance glucose monitoring technologies towards widespread clinical use.
Open Access: Yes
Comparison of three artificial intelligence methods for predicting 90% quantile interval of future insulin sensitivity of intensive care patients
Publication Name: IFAC Journal of Systems and Control
Publication Date: 2024-12-01
Volume: 30
Issue: Unknown
Page Range: Unknown
Description:
Insulin dosing of hyperglycemic patients in the intensive care unit (ICU) is a complex and nonlinear clinical control problem. Recent model-based glycemic control protocols predict a patient-specific and time-specific future insulin sensitivity distribution, which defines the future patient state in response to insulin and nutrition inputs. The prediction methods provide a 90% confidence interval for a future insulin sensitivity distribution for a given time horizon, making the prediction problem more specific compared to common prediction problems where the aim is to predict the expected value of the given stochastic parameter. This study proposes three alternative artificial intelligence-based insulin sensitivity prediction methods to improve the prediction accuracy and make prediction parameters better fit the clinical requirements. The proposed prediction methods use different neural network models: a classification deep neural network model, a Mixture Density Network model, and a Quantile Regression-based model. A large patient data set was used to create the neural network models, including 2357 patients and 92646 blood glucose measurements from three clinical sites (Christchurch, New Zealand, Gyula, Hungary, and Liege, Belgium). Prediction accuracy was assessed by statistical metrics expressing clinical requirements, as well as via validated in-silico virtual patient simulations comparing the clinical performance of a proven glycaemic control protocol using the alternative prediction methods to assess impact on glycemic control performance and thus the need for these alternative models.
Open Access: Yes
In-Silico Validation of Insulin Sensitivity Prediction by Neural Network-based Quantile Regression
Publication Name: IFAC Papersonline
Publication Date: 2024-09-01
Volume: 58
Issue: 24
Page Range: 368-373
Description:
High blood glucose levels and stress-induced hyperglycemia are common issues in intensive care units (ICU). Controlling blood glucose levels is crucial but challenging due to patient-specific variability. This challenge was addressed by developing model-based control protocols, which rely on identifying and predicting the patient-specific metabolic state. This study presents the in-silico simulation-based evaluation of a new artificial neural network-based insulin sensitivity (SI) prediction method. The models were trained on a dataset collected during clinical treatment using the stochastic-targeted (STAR) protocol and evaluated by simulating the clinical interventions on virtual patients created from retrospective clinical data. The results show the new models could be safely applied for SI prediction. Furthermore, the adopted method had very similar accuracy in the overall comparison of cohorts, with only minor differences noted in hypoglycemia events.
Open Access: Yes
High-Sensitivity SIW Sensor for Wide-Range Non-Invasive Blood Glucose Monitoring Using Complementary Split-Ring Resonator
Publication Name: Applied Biosciences
Publication Date: 2026-03-01
Volume: 5
Issue: 1
Page Range: Unknown
Description:
This work presents a compact microwave sensor for noninvasive blood glucose monitoring based on a substrate-integrated waveguide loaded with a complementary split-ring resonator on RO4350. The sensing principle uses shifts in resonance frequency and changes in S-parameters to track the dielectric dispersion of glucose-containing tissue. The resonator is constructed using Substrate-Integrated Waveguide (SIW) technology, which mimics the propagation characteristics of a conventional rectangular waveguide. To validate its versatility, the sensor implements three practical sample delivery modes: direct liquid contact with the sensing surface, a glass tube holder mounted over the active region, and a non-invasive fingertip interface. Electromagnetic simulations and benchtop measurements confirm clear glucose-dependent frequency shifts with stable matching and insertion levels. Across the physiological range of 20 to 200 mg·dL−1, the sensor exhibits clear glucose-dependent resonance shifts in all configurations. In direct contact mode, the resonance frequency shifts from 10.83 GHz to 10.45 GHz with sensitivities up to 2.47 MHz per mg·dL−1. The tube configuration shows a shift from 10.49 GHz to 10.38 GHz with sensitivity up to 0.80 MHz per mg·dL−1, while reducing contamination. In the non-invasive fingertip mode, the resonance shifts from 2.56 GHz to 2.52 GHz with sensitivities up to 0.25 MHz per mg·dL−1. These results confirm the sensor’s compactness, reliability, and suitability for portable, low-cost glucose monitoring. The results indicate that the proposed sensor can support practical continuous or spot monitoring and offers a clear path toward portable and low-cost glucose assessment.
Open Access: Yes
Development and experimental evaluation of single-port substrate integrated waveguide resonator with dual-parameter sensitivity for non-invasive blood glucose monitoring
Publication Name: Measurement Journal of the International Measurement Confederation
Publication Date: 2026-06-16
Volume: 278
Issue: Unknown
Page Range: Unknown
Description:
Current Blood Glucose (BG) monitoring techniques are invasive or semi-invasive and can impose financial and practical burden on patients. In this study a compact, non-invasive and single port Substrate Integrated Waveguide (SIW) loaded with X-shape has been present. The sensor resonant at 1.7 GHz, a dual parameter employed to evaluate the sensor by tracking the shift of resonance frequency in (MHz) and the reflection coefficient (dB) to detect glucose-induced changes in tissue permittivity. The device is designed using full-wave electromagnetic simulations with multilayer tissue models and validated experimentally on five human volunteers under controlled fasting and post-glucose conditions. Across the physiological range of 20–200 mg/dL, the sensor exhibits sensitivities up to 0.310 MHz per mg/dL and 0.333 dB per mg/dL, demonstrating consistent responsiveness to glucose variations. The results indicate that the proposed resonator can track glucose-related dielectric changes using a simple contact-based configuration. However, the measurements are influenced by subject-specific variability and sensor placement conditions, which currently limit generalization and repeatability. Further work is required to improve robustness, calibration, and validation on larger cohorts before practical deployment.
Open Access: Yes
