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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)
Publ. Version/Full Text DOI PMC
Open Access Gold (Paid Option)
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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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Complications Severity Index; Foot Ulceration; Skin; Density
ISSN (print) / ISBN 2157-846X
e-ISSN 2157-846X
Quellenangaben Volume: 7, Issue: 12, Pages: 1667-1682 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place London ; New York NY ; Tokyo
Non-patent literature Publications
Reviewing status Peer reviewed
Grants 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