Abstract
Noninvasive, glucose-monitoring technologies using infrared spectroscopy that have been studied typically require a calibration process that involves blood collection, which renders the methods somewhat invasive. We develop a truly noninvasive, glucose-monitoring technique using mid-infrared spectroscopy that does not require blood collection for calibration by applying domain adaptation (DA) using deep neural networks to train a model that associates blood glucose concentration with mid-infrared spectral data without requiring a training dataset labeled with invasive blood sample measurements. For realizing DA, the distribution of unlabeled spectral data for calibration is considered through adversarial update during training networks for regression to blood glucose concentration. This calibration improved the correlation coefficient between the true blood glucose concentrations and predicted blood glucose concentrations from 0.38 to 0.47. The result indicates that this calibration technique improves prediction accuracy for mid-infrared glucose measurements without any invasively acquired data.
Highlights
In recent years, the incidence of diabetes has increased worldwide, increasing the market demand for noninvasive blood glucose-monitoring technologies
We have developed a technique that transmits optical signals using an attenuated total re°ection (ATR) prism and a hollow opticalber[18] that e±ciently propagates mid-infrared light.[19]
We developed a method for calibrating mid-infrared spectral blood glucose data without training data derived from blood samples
Summary
The incidence of diabetes has increased worldwide, increasing the market demand for noninvasive blood glucose-monitoring technologies. Various methods have been proposed for noninvasive blood glucose measurements, including near-infrared sensing,[1,2,3] mid-infrared sensing,[4] Raman spectroscopy,[5] and photoacoustics.[6,7] Of these options, the mid-infrared light spectrum o®ers good detection accuracy because glucose absorbs light well at these wavelengths. Practical applications of noninvasive blood glucose measurement technologies are limited by the measurement accuracy[8] and require invasive calibration steps for practical use. We propose a novel method to calibrate noninvasive glucose measurements with spectral data alone, which could lead to a truly noninvasive blood glucose-monitoring system
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