Enhancing diagnosis of Hirschsprung's disease using deep learning from histological sections of post pull-through specimens: preliminary results.

Journal: Pediatric surgery international
PMID: 38019366

Abstract

PURPOSE: Accurate histological diagnosis in Hirschsprung disease (HD) is challenging, due to its complexity and potential for errors. In this study, we present an artificial intelligence (AI)-based method designed to identify ganglionic cells and hypertrophic nerves in HD histology.

Authors

  • Miriam Duci
    Division of Pediatric Surgery, Department of Women's and Children's Health, University of Padova, Via Giustiniani 2, 35128, Padova, Italy.
  • Alessia Magoni
    Department of Industrial Engineering, Padova University, Padova, Italy.
  • Luisa Santoro
    Surgical Pathology and Cytopathology Unit, Department of Medicine, Padova University, Padova, Italy.
  • Angelo Paolo Dei Tos
    Surgical Pathology & Cytopathology Unit, Department of Medicine - DIMED, University of Padua, Padua, Italy.
  • Piergiorgio Gamba
    Pediatric Surgery Unit, Medical University of Padua, Padua, Italy.
  • Francesca Uccheddu
    Department of Industrial Engineering, Padova University, Padova, Italy.
  • Francesco Fascetti-Leon
    Division of Pediatric Surgery, Department of Women's and Children's Health, University of Padova, Via Giustiniani 2, 35128, Padova, Italy. francesco.fascettileon@unipd.it.

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