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Title:

Dermal features derived from optoacoustic tomograms via machine learning correlate microangiopathy phenotypes with diabetes stage.

Document type:
Zeitschriftenaufsatz
Author(s):
Karlas, Angelos; Katsouli, Nikoletta; Fasoula, Nikolina-Alexia; Bariotakis, Michail; Chlis, Nikolaos-Kosmas; Omar, Murad; He, Hailong; Iakovakis, Dimitrios; Schäffer, Christoph; Kallmayer, Michael; Füchtenbusch, Martin; Ziegler, Annette; Eckstein, Hans-Henning; Hadjileontiadis, Leontios; Ntziachristos, Vasilis
Abstract:
Skin microangiopathy has been associated with diabetes. Here we show that skin-microangiopathy phenotypes in humans can be correlated with diabetes stage via morphophysiological cutaneous features extracted from raster-scan optoacoustic mesoscopy (RSOM) images of skin on the leg. We obtained 199 RSOM images from 115 participants (40 healthy and 75 with diabetes), and used machine learning to segment skin layers and microvasculature to identify clinically explainable features pertaining to differ...     »
Journal title:
Nature Biomedical Engineering
Journal title abbreviation:
Nat Biomed Eng
Year:
2023
Journal volume:
7
Journal issue:
12
Pages contribution:
1667-1682
Fulltext / DOI:
doi:10.1038/s41551-023-01151-w
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/38049470
WWW:
https://www.nature.com/articles/s41551-023-01151-w
Print-ISSN:
2157-846X
TUM Institution:
Klinik und Poliklinik für Vaskuläre und Endovaskuläre Chirurgie (Prof. Eckstein); Lehrstuhl für Biologische Bildgebung - Zusammenarbeit mit dem Helmholtz-Zentrum München (Prof. Ntziachristos)
CC license:
by, http://creativecommons.org/licenses/by/4.0
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