nach oben

Lasers in Medical Science

Erschienen in:

27.09.2022 | Letter to the Editor

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

verfasst von: Ivan A. Bratchenko, Lyudmila A. Bratchenko

Erschienen in: Lasers in Medical Science | Ausgabe 9/2022

Einloggen, um Zugang zu erhalten

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. …

N. González-Viveros, J. Castro-Ramos, P. Gómez-Gil et al (2022) Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks Lasers Med Sci. https://doi.org/10.1007/s10103-022-03633-w

Zhao J, Zeng H, Kalia S, Lui H (2019) Incorporating patient demographics into Raman spectroscopy algorithm improves in vivo skin cancer diagnostic specificity. Transl Biophotonics 1:e201900016. https://doi.org/10.1002/tbio.201900016CrossRef

Bratchenko IA, Bratchenko LA, Moryatov AA et al (2021) In vivo diagnosis of skin cancer with a portable Raman spectroscopic device. Exp dermatol 30:652–663. https://doi.org/10.1111/exd.14301CrossRefPubMed

Khristoforova YuA et al (2021) Optical biopsy of amelanotic melanoma with Raman and autofluorescence spectra stimulated by 785 nm laser excitation. J Biomed Photonics Eng 7(2):020308. https://doi.org/10.18287/JBPE21.07.020308CrossRef

Guevara E, Torres-Galván JC, Ramírez-Elías MG et al (2018) Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools. Biomed. Opt. Express 9(10):4998–5010CrossRefPubMedPubMedCentral

E. Guevara, J.C. Torres-Galván, F. J. González, et al (2022) Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre-diabetes and diabetes. Journal of Biophotonics, 15(9). https://doi.org/10.1002/jbio.202200055

Bratchenko LA, Bratchenko IA, Khristoforova YA et al (2021) Raman spectroscopy of human skin for kidney failure detection. J Biophotonics 14(2):e202000360. https://doi.org/10.1002/jbio.202000360CrossRefPubMed

Yakimov BP, Venets AV, Schleusener J, Fadeev VV, Lademann J, Shirshin EA, Darvin ME (2021) Blind source separation of molecular components of the human skin in vivo: non-negative matrix factorization of Raman microspectroscopy data. Anal 146:3185–3196. https://doi.org/10.1039/D0AN02480ECrossRef

Feng X, Moy AJ, Nguyen HTM et al (2017) Raman active components of skin cancer. Biomed Opt Express 8(6):2835–2850. https://doi.org/10.1364/BOE.8.002835CrossRefPubMedPubMedCentral

10.

Shao J, Lin M, Li Y et al (2012) In vivo blood glucose quantification using Raman spectroscopy. PLoS ONE 7(10):e48127. https://doi.org/10.1371/journal.pone.0048127CrossRefPubMedPubMedCentral

11.

J.W.KANG, Y.S. PARK, J. CHANG et al. (2020) SCIENCE ADVANCES. 6(4). https://doi.org/10.1126/sciadv.aay5206

12.

Bratchenko IA, Bratchenko LA (2021) Comment on “Combining derivative Raman with autofluorescence to improve the diagnosis performance of echinococcosis.” Spectrochim Acta A: Mol Biomol Spectros 252:119514. https://doi.org/10.1016/j.saa.2021.119514CrossRef

13.

Bratchenko IA, Bratchenko LA, Khristoforova YA et al (2022) Classification of skin cancer using convolutional neural networks analysis of Raman spectra. Com Methods Programs Biomed 219:106755. https://doi.org/10.1016/j.cmpb.2022.106755CrossRef

Titel: Comment on “Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks”
verfasst von: Ivan A. Bratchenko
Lyudmila A. Bratchenko
Publikationsdatum: 27.09.2022
Verlag: Springer London
Erschienen in: Lasers in Medical Science / Ausgabe 9/2022
Print ISSN: 0268-8921
Elektronische ISSN: 1435-604X
DOI: https://doi.org/10.1007/s10103-022-03650-9

Springer Medizin

Excerpt

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 9/2022

BMLA 2022 Annual Conference

Iron oxide nanoparticles coated with polydopamine as a potential nano-photothermal agent for treatment of melanoma cancer: an in vivo study

Anti-inflammatory effect of green photobiomodulation in human adipose-derived mesenchymal stem cells

Micro-Raman spectroscopy study of blood samples from myocardial infarction patients

NIR irradiation of human buccal fat pad adipose stem cells and its effect on TRP ion channels

Advanced methylene blue — nanoemulsions for in vitro photodynamic therapy on oral and cervical human carcinoma