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Karlas, A. ; Katsouli, N. ; Fasoula, N.-A. ; Bariotakis, M. ; Chlis, N.-K. ; Omar, M. ; He, H. ; Iakovakis, D.* ; Schäffer, C.* ; Kallmayer, M.* ; Füchtenbusch, M.* ; Ziegler, A.-G. ; Eckstein, H.H.* ; Hadjileontiadis, L.J.* ; Ntziachristos, V.

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

Nat. Bio. Eng. 7, 1667-1682 (2023)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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 different depths and scales of detail that provided the highest predictive power. Features in the dermal layer at the scale of detail of 0.1-1 mm (such as the number of junction-to-junction branches) were highly sensitive to diabetes stage. A 'microangiopathy score' compiling the 32 most-relevant features predicted the presence of diabetes with an area under the receiver operating characteristic curve of 0.84. The analysis of morphophysiological cutaneous features via RSOM may allow for the discovery of diabetes biomarkers in the skin and for the monitoring of diabetes status.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Complications Severity Index; Foot Ulceration; Skin; Density
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 2157-846X
e-ISSN 2157-846X
Zeitschrift Nature biomedical engineering
Quellenangaben Band: 7, Heft: 12, Seiten: 1667-1682 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort London ; New York NY ; Tokyo
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Biological and Medical Imaging (IBMI)
Institute of Computational Biology (ICB)
Institute of Diabetes Research (IDF)
POF Topic(s) 30205 - Bioengineering and Digital Health
30201 - Metabolic Health
Forschungsfeld(er) Enabling and Novel Technologies
Helmholtz Diabetes Center
PSP-Element(e) G-505500-001
G-505593-001
G-509200-001
G-503800-001
G-502100-001
Förderungen Helmholtz Zentrum Muenchen (Physician Scientists for Groundbreaking Projects)
DZHK (German Centre for Cardiovascular Research)
Graduate School of Quantitative Biosciences Munich (QBM)
European Research Council (ERC)
European Union's Horizon 2020 research and innovation programme
Khalifa University of Science and Technology, Abu Dhabi, UAE, Provost's Office Grant
DOI 10.1038/s41551-023-01151-w
WOS ID 001112820600002
Scopus ID 85178420467
PubMed ID 38049470
Erfassungsdatum 2023-12-15
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