A deep learning approach to direct immunofluorescence pattern recognition in autoimmune bullous diseases.

Journal: The British journal of dermatology
Published Date: Jul 16, 2024

Abstract

BACKGROUND: Artificial intelligence (AI) is reshaping healthcare, using machine and deep learning (DL) to enhance disease management. Dermatology has seen improved diagnostics, particularly in skin cancer detection, through the integration of AI. However, the potential of AI in automating immunofluorescence imaging for autoimmune bullous skin diseases (AIBDs) remains untapped. While direct immunofluorescence (DIF) supports diagnosis, its manual interpretation can hinder efficiency. The use of DL to classify DIF patterns automatically, including the intercellular (ICP) and linear pattern (LP), holds promise for improving the diagnosis of AIBDs.

Authors

  • Niccolò Capurro
    Section of Dermatology, Department of Health Sciences, University of Genoa, Genoa, Italy.
  • Vito Paolo Pastore
    Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genova, Genova, Italy.
  • Larbi Touijer
    MaLGa - DIBRIS, University of Genoa, Genoa, Italy.
  • Francesca Odone
    Department of Computer Science, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy.
  • Emanuele Cozzani
    Section of Dermatology, Department of Health Sciences, University of Genoa, Genoa, Italy.
  • Giulia Gasparini
    Section of Dermatology, Department of Health Sciences, University of Genoa, Genoa, Italy.
  • Aurora Parodi
    Section of Dermatology, Department of Health Sciences, University of Genoa, Genoa, Italy.

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