Machine Learning and Deep Neural Network Applications in the Thorax: Pulmonary Embolism, Chronic Thromboembolic Pulmonary Hypertension, Aorta, and Chronic Obstructive Pulmonary Disease.

Journal: Journal of thoracic imaging
Published Date:

Abstract

The radiologic community is rapidly integrating a revolution that has not fully entered daily practice. It necessitates a close collaboration between computer scientists and radiologists to move from concepts to practical applications. This article reviews the current littérature on machine learning and deep neural network applications in the field of pulmonary embolism, chronic thromboembolic pulmonary hypertension, aorta, and chronic obstructive pulmonary disease.

Authors

  • Martine Remy-Jardin
    Department of Thoracic Imagining-Hospital Calmette, University Center of Lille.
  • Jean-Baptiste Faivre
    Department of Thoracic Imagining-Hospital Calmette, University Center of Lille.
  • Rainer Kaergel
    Department of CT Research & Development, Siemens Healthcare GmbH, Forchheim, Germany.
  • Antoine Hutt
    Department of Thoracic Imagining-Hospital Calmette, University Center of Lille.
  • Paul Felloni
    Department of Thoracic Imagining-Hospital Calmette, University Center of Lille.
  • Suonita Khung
    Department of Thoracic Imagining-Hospital Calmette, University Center of Lille.
  • Anne-Laure Lejeune
    Department of Thoracic Imagining-Hospital Calmette, University Center of Lille.
  • Jessica Giordano
    Department of Thoracic Imagining-Hospital Calmette, University Center of Lille.
  • Jacques Remy
    Department of Thoracic Imagining-Hospital Calmette, University Center of Lille.