Using neural networks for non-invasive determination of glycated hemoglobin levels, illustrated by the application of an innovative portable glucometer in clinical practice

Abstract

BACKGROUND: In the last decade, there has been a significant increase in interest in non-invasive monitoring of blood glucose levels [1]. This is driven by the desire to reduce patient discomfort, as well as the risk of infections associated with traditional invasive methods [2]. Raman spectroscopy, considered as a promising approach for non-invasive measurements [3], combined with machine learning, has the potential to lead to more accurate and faster diagnostic methods for conditions related to glucose imbalances [4].

AIMS: Development and validation of a new portable glucometer based on Raman spectroscopy using machine learning methods for non-invasive determination of glycated hemoglobin (HbA_1c) levels.

MATERIALS AND METHODS: The study was conducted on a sample of 100 volunteers of different age groups and genders, with varying health statuses, including individuals with type 1 and type 2 diabetes and those without diabetes. To collect data, we used a portable device developed by us, based on the registration of Raman spectra with laser excitation at 638 nm. The data were analyzed using Support Vector Machine neural networks.

RESULTS: After processing the spectroscopic measurements using Support Vector Machine, the system showed sensitivity (95,7%) and specificity (84,2%) in determining HbA_1c levels comparable to traditional methods such as high-performance liquid chromatography. It was found that the algorithm is sufficiently adaptive and can be used across a wide range of skin types, regardless of the age and gender of the participants. The results suggest the possibility of using the developed device in clinical practice.

CONCLUSION: The developed portable glucometer based on Raman spectroscopy combined with machine learning algorithms could be a promising step towards non-invasive and continuous monitoring of glycemic levels in patients with diabetes.

Keywords

glycated hemoglobin / artificial intelligence / Raman spectroscopy / biomedical diagnostics / automatic pattern recognition / machine learning algorithms

References

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