Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma.

Journal: Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG
Published Date:

Abstract

BACKGROUND: Institutes of dermatopathology are faced with considerable challenges including a continuously rising numbers of submitted specimens and a shortage of specialized health care practitioners. Basal cell carcinoma (BCC) is one of the most common tumors in the fair-skinned western population and represents a major part of samples submitted for histological evaluation. Digitalizing glass slides has enabled the application of artificial intelligence (AI)-based procedures. To date, these methods have found only limited application in routine diagnostics. The aim of this study was to establish an AI-based model for automated BCC detection.

Authors

  • Nicole Duschner
    Dermatopathologie Duisburg Essen GmbH, Essen 45329, Germany.
  • Daniel Otero Baguer
    University of Bremen, Bremen 28359, Germany.
  • Maximilian Schmidt
    University of Bremen, Bremen 28359, Germany.
  • Klaus Georg Griewank
    Department of Dermatology, University Hospital Essen, Essen 45147, Germany; Dermatopathologie bei Mainz, Nieder-Olm 55268, Germany. Electronic address: klaus.griewank@uk-essen.de.
  • Eva Hadaschik
    Department of Dermatology, University Hospital Essen, Essen 45147, Germany.
  • Sonja Hetzer
    MVZ Dermatopathology Duisburg Essen, Essen, Germany.
  • Bettina Wiepjes
    MVZ Dermatopathology Duisburg Essen, Essen, Germany.
  • Jean Le'Clerc Arrastia
    Center for Technical Mathematics (ZeTeM), University of Bremen, Bremen, Germany.
  • Philipp Jansen
    Department of Dermatology, University Hospital Essen, Essen, Germany.
  • Peter Maaß
    Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany.
  • Jörg Schaller
    Dermatopathologie Duisburg Essen GmbH, Essen 45329, Germany.