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European Radiology
Published in: European Radiology 11/2020

Open Access 01-11-2020 | Idiopathic Pulmonary Fibrosis | Imaging Informatics and Artificial Intelligence

Deep learning in interstitial lung disease—how long until daily practice

Authors: Ana Adriana Trusculescu, Diana Manolescu, Emanuela Tudorache, Cristian Oancea

Published in: European Radiology | Issue 11/2020

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Abstract

Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis of interstitium, with clinical, radiological, and pathological overlapping features. These are an important cause of morbidity and mortality among lung diseases. This review describes computer-aided diagnosis systems centered on deep learning approaches that improve the diagnostic of interstitial lung diseases. We highlighted the challenges and the implementation of important daily practice, especially in the early diagnosis of idiopathic pulmonary fibrosis (IPF). Developing a convolutional neuronal network (CNN) that could be deployed on any computer station and be accessible to non-academic centers is the next frontier that needs to be crossed. In the future, early diagnosis of IPF should be possible. CNN might not only spare the human resources but also will reduce the costs spent on all the social and healthcare aspects of this deadly disease.
Key Points
• Deep learning algorithms are used in pattern recognition of different interstitial lung diseases.
• High-resolution computed tomography plays a central role in the diagnosis and in the management of all interstitial lung diseases, especially fibrotic lung disease.
• Developing an accessible algorithm that could be deployed on any computer station and be used in non-academic centers is the next frontier in the early diagnosis of idiopathic pulmonary fibrosis.
Appendix
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Metadata
Title
Deep learning in interstitial lung disease—how long until daily practice
Authors
Ana Adriana Trusculescu
Diana Manolescu
Emanuela Tudorache
Cristian Oancea
Publication date
01-11-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 11/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-06986-4

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