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Journal Articles Diagnostic and Interventional Imaging Year : 2023

Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution

Aissam Djahnine
Carole Lazarus
  • Function : Author
Mathieu Lederlin
  • Function : Author
Sébastien Mulé
Rafael Wiemker
Salim Si-Mohamed
  • Function : Author
Emilien Jupin-Delevaux
Olivier Nempont
  • Function : Author
Youssef Skandarani
  • Function : Author
Mathieu de Craene
  • Function : Author
Segbedji Goubalan
  • Function : Author
Caroline Raynaud
  • Function : Author
Amira Ben Afia
  • Function : Author
Clement Fabre
  • Function : Author
Gilbert Ferretti
  • Function : Author
Constance de Margerie
Pierre Berge
Renan Liberge
Nicolas Elbaz
  • Function : Author
Maxime Blain
Pierre-Yves Brillet
Guillaume Chassagnon
  • Function : Author
Farah Cadour
  • Function : Author
Caroline Caramella
  • Function : Author
Mostafa El Hajjam
  • Function : Author
Samia Boussouar
  • Function : Author
Joya Hadchiti
Xavier Fablet
  • Function : Author
Antoine Khalil
  • Function : Author
Alain Luciani
Nathalie Lassau
  • Function : Author
Loic Boussel

Abstract

The purpose of this study was to propose a deep learning-based approach to detect pulmonary embolism and quantify its severity using the Qanadli score and the right-to-left ventricle diameter (RV/LV) ratio on three-dimensional (3D) computed tomography pulmonary angiography (CTPA) examinations with limited annotations.
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Dates and versions

hal-04396326 , version 1 (15-01-2024)

Identifiers

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Aissam Djahnine, Carole Lazarus, Mathieu Lederlin, Sébastien Mulé, Rafael Wiemker, et al.. Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution. Diagnostic and Interventional Imaging, In press, ⟨10.1016/j.diii.2023.09.006⟩. ⟨hal-04396326⟩
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