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Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications.

Authors
  • Ather, S1
  • Kadir, T2
  • Gleeson, F3
  • 1 Department of Radiology, Churchill Hospital, Oxford, UK.
  • 2 Optellum Ltd, Oxford Centre of Innovation, Oxford, UK.
  • 3 National Consortium of Intelligent Medical Imaging, UK; Department of Oncology, University of Oxford, UK. Electronic address: [email protected]
Type
Published Article
Journal
Clinical radiology
Publication Date
Jan 01, 2020
Volume
75
Issue
1
Pages
13–19
Identifiers
DOI: 10.1016/j.crad.2019.04.017
PMID: 31202567
Source
Medline
Language
English
License
Unknown

Abstract

Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date back to the 1960s, and in the subsequent years, the main application of these techniques has been the detection and classification of pulmonary nodules. In addition, there have been other less intensely researched applications, such as the diagnosis of interstitial lung disease, chronic obstructive pulmonary disease, and the detection of pulmonary emboli. Despite extensive literature on the use of convolutional neural networks in thoracic imaging over the last few decades, we are yet to see these systems in use in clinical practice. The article reviews current state-of-the-art applications of AI and in detection, classification, and follow-up of pulmonary nodules and how deep-learning techniques might influence these going forward. Finally, we postulate the impact of these advancements on the role of radiologists and the importance of radiologists in the development and evaluation of these techniques. Copyright © 2019. Published by Elsevier Ltd.

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