Chronic cholestasis detection by a novel tool: automated analysis of cytokeratin 7-stained liver specimens.

Journal: Diagnostic pathology
PMID: 33957930

Abstract

BACKGROUND: The objective was to build a novel method for automated image analysis to locate and quantify the number of cytokeratin 7 (K7)-positive hepatocytes reflecting cholestasis by applying deep learning neural networks (AI model) in a cohort of 210 liver specimens. We aimed to study the correlation between the AI model's results and disease progression. The cohort of liver biopsies which served as a model of chronic cholestatic liver disease comprised of patients diagnosed with primary sclerosing cholangitis (PSC).

Authors

  • Nelli Sjöblom
    Department of Pathology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 3, 00290, Helsinki, Finland. nelli.sjoblom@hus.fi.
  • Sonja Boyd
    Department of Pathology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 3, 00290, Helsinki, Finland.
  • Anniina Manninen
    Aiforia Technologies Oy, Tukholmankatu 8, 000290, Helsinki, Finland.
  • Anna Knuuttila
    Aiforia Technologies Oy, Tukholmankatu 8, 000290, Helsinki, Finland.
  • Sami Blom
    Biomedicum, Fimmic Oy, Helsinki, Finland.
  • Martti Färkkilä
    Department of Gastroenterology, University of Helsinki and Helsinki University Hospital, 00290, Helsinki, Finland.
  • Johanna Arola

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