Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical makeup and heterogeneity of biological tissues. Combining this novel contrast mechanism in microscopy with the use of artificial intelligence can transform the practice of histopathology, which currently relies largely on human examination of morphologic patterns within stained tissue. First, this review summarizes IR imaging instrumentation especially suited to histopathology, analyses of its performance, and major trends. Second, an overview of data processing methods and application of machine learning is given, with an emphasis on the emerging use of deep learning. Third, a discussion on workflows in pathology is provided, with four categories proposed based on the complexity of methods and the analytical performance needed. Last, a set of guidelines, termed experimental and analytical specifications for spectroscopic imaging in histopathology, are proposed to help standardize the diversity of approaches in this emerging area.

Original languageEnglish (US)
Pages (from-to)205-230
Number of pages26
JournalAnnual Review of Analytical Chemistry
StatePublished - Jun 14 2023


  • chemical imaging
  • deep learning
  • digital pathology
  • imaging
  • infrared spectroscopy
  • machine learning

ASJC Scopus subject areas

  • Analytical Chemistry


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