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 deve...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 midinfrared 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 coeffcient 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.展开更多
The authors would like to apologize for some mistakes in the letter on Chinese Optics Letters vol. 12, no. 11, page 111701 and wish to make the corrections described below：
文摘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 midinfrared 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 coeffcient 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.
文摘The authors would like to apologize for some mistakes in the letter on Chinese Optics Letters vol. 12, no. 11, page 111701 and wish to make the corrections described below：
