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Comment on “Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks”

  • 27-09-2022
  • Letter to the Editor
Published in:

Excerpt

In the paper by González-Viveros et al. [1], the authors demonstrated combination of conventional Raman (CR) spectroscopy and machine learning methods for the discriminating of healthy, prediabetes, and type 2 diabetes (T2D) patients based on the glycated hemoglobin (HbA1c) estimations. The authors utilize cost-effective Raman spectrometer (with signal-to-noise ratio about 3) for in vivo skin measurements. The authors demonstrated extremely high performance of the proposed technique; however, the presented results may be treated incorrectly due to the overestimation of the proposed classification models. …
Title
Comment on “Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks”
Authors
Ivan A. Bratchenko
Lyudmila A. Bratchenko
Publication date
27-09-2022
Publisher
Springer London
Published in
Lasers in Medical Science / Issue 9/2022
Print ISSN: 0268-8921
Electronic ISSN: 1435-604X
DOI
https://doi.org/10.1007/s10103-022-03650-9
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