Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT

To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity.

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Bibliographic Details
Main Authors:	Almeida, Silvia D. (Author) , Norajitra, Tobias (Author) , Lüth, Carsten T. (Author) , Wald, Tassilo (Author) , Weru, Vivienn (Author) , Nolden, Marco (Author) , Jäger, Paul F. (Author) , Stackelberg, Oyunbileg von (Author) , Heußel, Claus Peter (Author) , Weinheimer, Oliver (Author) , Biederer, Jürgen (Author) , Kauczor, Hans-Ulrich (Author) , Maier-Hein, Klaus H. (Author)
Format:	Article (Journal)
Language:	English
Published:	2024
In:	European radiology Year: 2024, Volume: 34, Issue: 7, Pages: 4379–4392
ISSN:	1432-1084
DOI:	10.1007/s00330-023-10540-3
Online Access:	Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s00330-023-10540-3
Author Notes:	Silvia D. Almeida, Tobias Norajitra, Carsten T. Lüth, Tassilo Wald, Vivienn Weru, Marco Nolden, Paul F. Jäger, Oyunbileg von Stackelberg, Claus Peter Heußel, Oliver Weinheimer, Jürgen Biederer, Hans-Ulrich Kauczor and Klaus Maier-Hein

Description
Summary:	To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity.
Item Description:	Gesehen am 18.03.2024
Physical Description:	Online Resource
ISSN:	1432-1084
DOI:	10.1007/s00330-023-10540-3