Artificial neural networks trained on simulated multispectral data for real-time imaging of skin microcirculatory blood oxygen saturation.

Journal: Journal of biomedical optics
PMID: 38989257

Abstract

SIGNIFICANCE: Imaging blood oxygen saturation ( ) in the skin can be of clinical value when studying ischemic tissue. Emerging multispectral snapshot cameras enable real-time imaging but are limited by slow analysis when using inverse Monte Carlo (MC), the gold standard for analyzing multispectral data. Using artificial neural networks (ANNs) facilitates a significantly faster analysis but requires a large amount of high-quality training data from a wide range of tissue types for a precise estimation of .

Authors

  • Marcus Larsson
    Linköping University, Department of Biomedical Engineering, Linköping, Sweden.
  • Maria Ewerlöf
    Linköping University, Department of Biomedical Engineering, Linköping, Sweden.
  • E Göran Salerud
    Linköping University, Department of Biomedical Engineering, Linköping, Sweden.
  • Tomas Strömberg
    Linköping University, Department of Biomedical Engineering, Linköping, Sweden.
  • Ingemar Fredriksson
    Linköping University, Department of Biomedical Engineering, Linköping, Sweden.

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