Abstract
According to the International Diabetes Federation, 530 million people worldwide have diabetes, with more than 6.7 million reported deaths in 2021. Monitoring blood glucose levels is essential for individuals with diabetes, and developing noninvasive monitors has been a long-standing aspiration in diabetes management. The ideal method for monitoring diabetes is to obtain the glucose concentration level with a fast, accurate, and pain-free measurement that does not require blood drawing or a surgical operation. Multiple noninvasive glucose detection techniques have been developed, including bio-impedance spectroscopy, electromagnetic sensing, and metabolic heat conformation. Nevertheless, reliability and consistency challenges were reported for these methods due to ambient temperature and environmental condition sensitivity. Among all the noninvasive glucose detection techniques, optical spectroscopy has rapidly advanced. A photoacoustic system has been developed using a single wavelength quantum cascade laser, lasing at a glucose fingerprint of 1080 cm for noninvasive glucose monitoring. The system has been examined using artificial skin phantoms, covering the normal and hyperglycemia blood glucose ranges. The detection sensitivity of the system has been improved to mg/dL using a single wavelength for the entire range of blood glucose. Machine learning has been employed to detect glucose levels using photoacoustic spectroscopy in skin samples. Ensemble machine learning models have been developed to measure glucose concentration using classification techniques. The model has achieved a 90.4% prediction accuracy with 100% of the predicted data located in zones A and B of Clarke’s error grid analysis. This finding fulfills the US Food and Drug Administration requirements for glucose monitors.
Highlights
Diabetes mellitus, commonly known as diabetes, is a metabolic disorder that elevates the glucose percentage in the blood, caused by a dysfunction in the production or effectiveness of insulin in the body
Different regression models have been employed for glucose detection, such as partial least square (PLS) [26,36], principal component (PC) [28], multiple linear regression (MLR) [37], and artificial neural networks (ANNs) [38,39]
A single wavelength Quantum cascade lasers (QCLs) has been employed in a PA and MIR spectroscopy on the glucose fingerprint of 1080 cm−1 for noninvasive glucose monitoring
Summary
Commonly known as diabetes, is a metabolic disorder that elevates the glucose percentage in the blood, caused by a dysfunction in the production (type-1) or effectiveness (type-2) of insulin in the body. The blood glucose concentration can be potentially measured directly from blood, serum, plasma, urine, saliva, and tear liquid, as per [2–6] It can be directly determined from the interstitial fluid (ISF) [7], located underneath the skin in the epidermis layer. Infrared (IR) spectroscopy, including the NIR and MIR regimes, is being developed as an alternative approach to invasive glucose meters [17]. The combination of MIR and PA spectroscopy has demonstrated promising potential for substituting the invasive glucose monitoring technology [19–22]. An ensemble machine learning model has been developed to detect the glucose concentration of the skin samples using classification techniques. The model has achieved 90.4% prediction accuracy with 100% of the predicted data located in zones A and B of Clarke’s error grid analysis (EGA) This finding fulfills the FDA requirements for glucose monitors. G. conc.: glucose concentration, P: peak, Bg: background, R.O.: regression only
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