Accurate Post-Calibration Predictions for Noninvasive Glucose Measurements in People Using Confocal Raman Spectroscopy
Research output: Contribution to journal › Journal article › Research › peer-review
Documents
- Fulltext
Final published version, 4.01 MB, PDF document
In diabetes prevention and care, invasiveness of glucose measurement impedes efficient therapy and hampers the identification of people at risk. Lack of calibration stability in non-invasive technology has confined the field to short-term proof of principle. Addressing this challenge, we demonstrate the first practical use of a Raman-based and portable non-invasive glucose monitoring device used for at least 15 days following calibration. In a home-based clinical study involving 160 subjects with diabetes, the largest of its kind to our knowledge, we find that the measurement accuracy is insensitive to age, sex, and skin color. A subset of subjects with type 2 diabetes highlights promising real-life results with 99.8% of measurements within A + B zones in the consensus error grid and a mean absolute relative difference of 14.3%. By overcoming the problem of calibration stability, we remove the lingering uncertainty about the practical use of non-invasive glucose monitoring, boding a new, non-invasive era in diabetes monitoring.
Original language | English |
---|---|
Journal | ACS Sensors |
Volume | 8 |
Issue number | 3 |
Pages (from-to) | 1272-1279 |
Number of pages | 8 |
ISSN | 2379-3694 |
DOIs | |
Publication status | Published - 2023 |
Bibliographical note
Funding Information:
The authors wish to acknowledge the support from the colleagues at RSP Systems, Steno Diabetes Center in Odense and Copenhagen, and the Institute for Diabetes Technology in Ulm. The staff at KLIFO is recognized for the support in execution of the trial.
Publisher Copyright:
© 2023 The Authors. Published by American Chemical Society.
- calibration stability, diabetes, in vivo Raman spectroscopy, multivariate data analysis, non-invasive glucose monitoring, portable sensor, tissue diagnostics
Research areas
ID: 362330061