Universität zu Köln

dos Santos, Daniel Pinto, Brodehl, Sebastian, Baessler, Bettina ORCID: 0000-0002-3244-3864, Arnhold, Gordon, Dratsch, Thomas, Chon, Seung-Hun ORCID: 0000-0002-8923-6428, Mildenberger, Peter and Jungmann, Florian (2019). Structured report data can be used to develop deep learning algorithms: a proof of concept in ankle radiographs. Insights Imaging, 10 (1). LONDON: SPRINGEROPEN. ISSN 1869-4101

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Abstract

Background Data used for training of deep learning networks usually needs large amounts of accurate labels. These labels are usually extracted from reports using natural language processing or by time-consuming manual review. The aim of this study was therefore to develop and evaluate a workflow for using data from structured reports as labels to be used in a deep learning application. Materials and methods We included all plain anteriorposterior radiographs of the ankle for which structured reports were available. A workflow was designed and implemented where a script was used to automatically retrieve, convert, and anonymize the respective radiographs of cases where fractures were either present or absent from the institution's picture archiving and communication system (PACS). These images were then used to retrain a pretrained deep convolutional neural network. Finally, performance was evaluated on a set of previously unseen radiographs. Results Once implemented and configured, completion of the whole workflow took under 1 h. A total of 157 structured reports were retrieved from the reporting platform. For all structured reports, corresponding radiographs were successfully retrieved from the PACS and fed into the training process. On an unseen validation subset, the model showed a satisfactory performance with an area under the curve of 0.850 (95% CI 0.634-1.000) for detection of fractures. Conclusion We demonstrate that data obtained from structured reports written in clinical routine can be used to successfully train deep learning algorithms. This highlights the potential role of structured reporting for the future of radiology, especially in the context of deep learning.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code dos Santos, Daniel Pinto UNSPECIFIED UNSPECIFIED UNSPECIFIED Brodehl, Sebastian UNSPECIFIED UNSPECIFIED UNSPECIFIED Baessler, Bettina UNSPECIFIED orcid.org/0000-0002-3244-3864 UNSPECIFIED Arnhold, Gordon UNSPECIFIED UNSPECIFIED UNSPECIFIED Dratsch, Thomas UNSPECIFIED UNSPECIFIED UNSPECIFIED Chon, Seung-Hun UNSPECIFIED orcid.org/0000-0002-8923-6428 UNSPECIFIED Mildenberger, Peter UNSPECIFIED UNSPECIFIED UNSPECIFIED Jungmann, Florian UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-141064
DOI:	10.1186/s13244-019-0777-8
Journal or Publication Title:	Insights Imaging
Volume:	10
Number:	1
Date:	2019
Publisher:	SPRINGEROPEN
Place of Publication:	LONDON
ISSN:	1869-4101
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language RADIOLOGY Multiple languages Radiology, Nuclear Medicine & Medical Imaging Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/14106