An open source pipeline for quantitative immunohistochemistry image analysis of inflammatory skin disease using artificial intelligence.

Journal: Journal of the European Academy of Dermatology and Venereology : JEADV
Published Date: Dec 3, 2022

Abstract

BACKGROUND: The application of artificial intelligence (AI) to whole slide images has the potential to improve research reliability and ultimately diagnostic efficiency and service capacity. Image annotation plays a key role in AI and digital pathology. However, the work-streams required for tissue-specific (skin) and immunostain-specific annotation has not been extensively studied compared with the development of AI algorithms.

Authors

  • Yuchun Ding
    Interdisciplinary Computing and Complex Biosystems Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK.
  • Gaurav Dhawan
    Institute of Translational and Clinical Medicine, Newcastle University Medical School, Newcastle upon Tyne, UK.
  • Claire Jones
    MRC/EPSRC, Molecular Pathology Node, Department of Pathology, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
  • Thomas Ness
    MRC/EPSRC, Molecular Pathology Node, Department of Pathology, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
  • Esme Nichols
    Institute of Translational and Clinical Medicine, Newcastle University Medical School, Newcastle upon Tyne, UK.
  • Natalio Krasnogor
    Interdisciplinary Computing and Complex Biosystems Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK.
  • Nick J Reynolds
    Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom.

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