Authors:	Jorg, Tobias Kämpgen, Benedikt Feiler, Dennis Müller, Lukas Düber, Christoph Mildenberger, Peter Jungmann, Florian
Title:	Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing
Online publication date:	17-Aug-2023
Year of first publication:	2023
Language:	english
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.
DDC:	610 Medizin 610 Medical sciences
Institution:	Johannes Gutenberg-Universität Mainz
Department:	FB 04 Medizin
Place:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-9418
Version:	Published version
Publication type:	Zeitschriftenaufsatz
Document type specification:	Scientific article
License:	CC BY
Information on rights of use:	https://creativecommons.org/licenses/by/4.0/
Journal:	Insights into imaging 14
Pages or article number:	47
Publisher:	Springer
Publisher place:	Berlin u.a.
Issue date:	2023
ISSN:	1869-4101
Publisher DOI:	10.1186/s13244-023-01392-y
Appears in collections:	DFG-491381577-G