Deep learning for automatic quantification of lung abnormalities in COVID-19 patients: First experience and correlation with clinical parameters

Deep learning for automatic quantification of lung abnormalities in COVID-19 patients: First experience and correlation with clinical parameters

Rationale and objectives: To demonstrate the first experience of a deep learning-based algorithm for automatic quantification of lung parenchymal abnormalities in chest CT of COVID-19 patients and to correlate quantitative results with clinical and laboratory parameters. Materials and methods: We re...

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Bibliographic Details
Main Authors: Victor Mergen, Adrian Kobe, Christian Blüthgen, André Euler, Thomas Flohr, Thomas Frauenfelder, Hatem Alkadhi, Matthias Eberhard
Format: Article
Language:English
Published: Elsevier 2020-01-01
Series:European Journal of Radiology Open
Subjects:
Computed tomography
COVID-19
Deep learning
Lung infection
Online Access:http://www.sciencedirect.com/science/article/pii/S2352047720300617

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http://www.sciencedirect.com/science/article/pii/S2352047720300617

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