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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)
DOI PMC
Creative Commons Lizenzvertrag
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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
Korrespondenzautor
Schlagwörter Complications Severity Index; Foot Ulceration; Skin; Density
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
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Biological and Medical Imaging (IBMI)
Institute of Computational Biology (ICB)
Institute of Diabetes Research Type 1 (IDF)
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