Deep learning for giant cell arteritis diagnosis on temporal artery biopsy.

Allergy & Immunology Dermatology Hematology Oncology/Hematology Pathology
Journal: Computers in biology and medicine
Published Date: Jul 8, 2025

Abstract

OBJECTIVES: Giant Cell Arteritis (GCA) is a vasculitis affecting large and medium-caliber arteries, requiring early and accurate diagnosis to prevent serious complications. Temporal artery biopsy (TAB) is the gold standard for histopathological diagnosis, but its evaluation is challenging, time-consuming, and requires significant expertise. This study aimed to assess the accuracy of a deep learning model in diagnosing GCA from TAB images.

Authors

  • Raphaël Bourgade
    Department of Pathology, University Hospital of Nantes, Nantes, France. Electronic address: [email protected].
  • Mounia Elhannani
    Department of Pathology, University Hospital of Nantes, 9 Quai Moncousu cedex 01, 44093, Nantes, France.
  • Delphine Loussouarn
    Department of Pathology, University Hospital of Nantes, Nantes, France.
  • Alexis Guédon
    Biosurgical Research Lab (Carpentier Foundation), European Georges-Pompidou Hospital, INSERM UMR_S 1140, University of Paris, Paris, France.
  • Renaud Péteri
    Laboratoire Mathématiques, Image et Applications EA 3165, Université de La Rochelle, La Rochelle, France.
  • Caroline Allix-Béguec
    Clinical Research Unit, Groupe Hospitalier de la Rochelle Ré Aunis, La Rochelle, France.
  • Michel Berthier
    La Rochelle Université, Laboratoire MIA, 23 Avenue A. Einstein BP 33060 cedex, 17031, La Rochelle, France.
  • Claire Toquet
    Department of Pathology, University Hospital of Nantes, Nantes, France.
  • Olivier Espitia
    Department of Internal Medicine, CHU Nantes, France.
  • Christophe Roncato
    Vascular Medicine Department, Groupe Hospitalier de la Rochelle Ré Aunis, La Rochelle, France. [email protected].
  • Charles Lépine
    Department of Pathology, University Hospital of Nantes, 9 Quai Moncousu cedex 01, 44093, Nantes, France.

Keywords

External Resources

Popular Topics
Recent Journals