Multicentric clinical evaluation of a computed tomography-based fully automated deep neural network for aortic maximum diameter and volumetric measurements.

Journal: Journal of vascular surgery
PMID:

Abstract

OBJECTIVE: This study aims to evaluate a fully automatic deep learning-based method (augmented radiology for vascular aneurysm [ARVA]) for aortic segmentation and simultaneous diameter and volume measurements.

Authors

  • Thomas J Postiglione
    Aortic Centre, Hôpital Marie Lannelongue, Groupe Hospitalier Paris Saint Joseph, Université Paris Saclay, Paris, France.
  • Enora Guillo
    Radiology Department, Hopital Cochin - AP-HP. Centre Université de Paris, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France.
  • Alexandre Heraud
    Radiology Department, Groupe Hospitalier Paris Saint Joseph, Paris, France.
  • Alexandre Rossillon
    Vascular Surgery, CHU La Timone, Marseille, France.
  • Michel Bartoli
    Vascular Surgery, CHU La Timone, Marseille, France.
  • Guillaume Herpe
    Department of Radiology, University Hospital of Poitiers, 2 rue de la Milétrie, 86021 Poitiers, France.
  • Chloé Adam
    Incepto Medical, Paris, France.
  • Dominique Fabre
    Aortic Centre, Hôpital Marie Lannelongue, Groupe Hospitalier Paris Saint Joseph, Université Paris Saclay, Paris, France.
  • Roberto Ardon
    Incepto Medical, Paris, France.
  • Arshid Azarine
    Radiology Department, Groupe Hospitalier Paris Saint Joseph, Paris, France.
  • Stephan Haulon
    Aortic Centre, Hôpital Marie Lannelongue, Groupe Hospitalier Paris Saint Joseph, Université Paris Saclay, Paris, France. Electronic address: haulon@hotmail.com.