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Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training

Crossref DOI link: https://doi.org/10.1007/s00330-021-07833-w

Published Online: 2021-03-27

Published Print: 2021-10

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Rueckel, Johannes https://orcid.org/0000-0002-6695-2958
Huemmer, Christian

Fieselmann, Andreas

Ghesu, Florin-Cristian

Mansoor, Awais

Schachtner, Balthasar

Wesp, Philipp

Trappmann, Lena

Munawwar, Basel

Ricke, Jens

Ingrisch, Michael

Sabel, Bastian O.
Funding

Funding for this research was provided by:

Siemens Healthineers
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Text and Data Mining valid from 2021-03-27

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Article History

Received: 5 September 2020

Revised: 6 January 2021

Accepted: 24 February 2021

First Online: 27 March 2021

Declarations

:

: Dr. Johannes Rückel, B.Sc.

: LMU Department of Radiology received research grant from Siemens Healthineers. Johannes Rueckel and Bastian Sabel received funding for speaker’s activity by Siemens Healthineers.

: No complex statistical methods were necessary for this paper. Several authors have significant expertise for the applied methods.

: Does not apply—retrospective study.

: Approval of the institutional ethics commission was obtained for this study.

: Study subjects have been partially reported:Johannes Rueckel, Lena Trappmann, Balthasar Schachtner, et al: Impact of Confounding Thoracic Tubes and Pleural Dehiscence Extent on Artificial Intelligence Pneumothorax Detection in Chest Radiographs. Investigative Radiology 2020

: • retrospective• diagnostic study• performed at one institution

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