Gutenberg Open Science: Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-9418

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DC Field	Value	Language
dc.contributor.author	Jorg, Tobias	-
dc.contributor.author	Kämpgen, Benedikt	-
dc.contributor.author	Feiler, Dennis	-
dc.contributor.author	Müller, Lukas	-
dc.contributor.author	Düber, Christoph	-
dc.contributor.author	Mildenberger, Peter	-
dc.contributor.author	Jungmann, Florian	-
dc.date.accessioned	2023-08-17T10:39:12Z	-
dc.date.available	2023-08-17T10:39:12Z	-
dc.date.issued	2023	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/9436	-
dc.description.abstract	Background Structured reporting (SR) is recommended in radiology, due to its advantages over free-text reporting (FTR). However, SR use is hindered by insufficient integration of speech recognition, which is well accepted among radiologists and commonly used for unstructured FTR. SR templates must be laboriously completed using a mouse and keyboard, which may explain why SR use remains limited in clinical routine, despite its advantages. Artificial intelligence and related fields, like natural language processing (NLP), offer enormous possibilities to facilitate the imaging workflow. Here, we aimed to use the potential of NLP to combine the advantages of SR and speech recognition. Results We developed a reporting tool that uses NLP to automatically convert dictated free text into a structured report. The tool comprises a task-oriented dialogue system, which assists the radiologist by sending visual feedback if relevant findings are missed. The system was developed on top of several NLP components and speech recognition. It extracts structured content from dictated free text and uses it to complete an SR template in RadLex terms, which is displayed in its user interface. The tool was evaluated for reporting of urolithiasis CTs, as a use case. It was tested using fictitious text samples about urolithiasis, and 50 original reports of CTs from patients with urolithiasis. The NLP recognition worked well for both, with an F1 score of 0.98 (precision: 0.99; recall: 0.96) for the test with fictitious samples and an F1 score of 0.90 (precision: 0.96; recall: 0.83) for the test with original reports. Conclusion Due to its unique ability to integrate speech into SR, this novel tool could represent a major contribution to the future of reporting.	en_GB
dc.description.sponsorship	Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491381577	de
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.title	Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-9418	-
jgu.type.contenttype	Scientific article	de
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 04 Medizin	de
jgu.organisation.number	2700	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Insights into imaging	de
jgu.journal.volume	14	de
jgu.pages.alternative	47	de
jgu.publisher.year	2023	-
jgu.publisher.name	Springer	de
jgu.publisher.place	Berlin u.a.	de
jgu.publisher.issn	1869-4101	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	610	de
jgu.publisher.doi	10.1186/s13244-023-01392-y	de
jgu.organisation.ror	https://ror.org/023b0x485	-
Appears in collections:	DFG-491381577-G

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