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

Angelos Karlas, Nikoletta Katsouli, Nikolina Alexia Fasoula, Michael Bariotakis, Nikolaos Kosmas Chlis, Murad Omar, Hailong He, Dimitrios Iakovakis, Christoph J. Schaffer, Michael A. Kallmayer, Martin Füchtenbusch, Annette Ziegler, Hans Henning Eckstein, Leontios J. Hadjileontiadis, Vasilis Ntziachristos

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 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. © 2023, The Author(s).
Original languageAmerican English
Pages (from-to)1667-1682
Number of pages16
JournalNature Biomedical Engineering
Volume7
Issue number12
DOIs
StatePublished - 2023

Keywords

  • Machine learning
  • Dermal layers
  • Machine-learning
  • Mesoscopies
  • Predictive power
  • Raster scans
  • Receiver operating characteristic curves
  • Relevant features
  • Skin layer
  • Skin microvasculature
  • Image segmentation
  • diabetes mellitus
  • diagnostic imaging
  • human
  • machine learning
  • phenotype
  • photoacoustics
  • procedures
  • skin
  • vascularization
  • Diabetes Mellitus
  • Humans
  • Machine Learning
  • Phenotype
  • Photoacoustic Techniques
  • Skin

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