Artificial Intelligence for Cardiothoracic Imaging: Overview of Current and Emerging Applications.

Journal: Seminars in roentgenology
Published Date:

Abstract

Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremendous interest and increased incorporation of artificial intelligence into various industries, including healthcare. As a result, there has been an exponential rise in the number of research articles and industry participants producing models intended for a variety of applications in medical imaging, which can be challenging to navigate for radiologists. In thoracic imaging, multiple applications are being evaluated for chest radiography and computed tomography and include applications for lung nodule evaluation and cancer imaging, quantifying diffuse lung disorders, and cardiac imaging, to name a few. This review aims to provide an overview of current clinical AI models, focusing on the most common clinical applications of AI in cardiothoracic imaging.

Authors

  • Bruno Hochhegger
    Pontificia Universidade Catolica do Rio Grande do Sul.
  • Romulo Pasini
    Department of Radiology, University of Florida, Gainesville, FL.
  • Alysson Roncally Carvalho
    IDOR, Rio de Janeiro, Brazil.
  • Rosana Rodrigues
    IDOR, Rio de Janeiro, Brazil.
  • Stephan Altmayer
    Pontificia Universidade Catolica do Rio Grande do Sul.
  • Leonardo Kayat Bittencourt
    Department of Radiology, Case Western Reserve University, School of Medicine, Cleveland, OH.
  • Edson Marchiori
    Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
  • Reza Forghani
    Department of Radiology, McGill University Health Centre, 1001 Decarie Blvd, Room C02.5821, Montreal, QC, Canada H4A 3J1; Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of the McGill University Health Centre, Montreal, Canada; Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada; and Department of Otolaryngology-Head and Neck Surgery, McGill University, Montreal, Canada.