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Autoren: Jorg, Tobias
Kämpgen, Benedikt
Feiler, Dennis
Müller, Lukas
Düber, Christoph
Mildenberger, Peter
Jungmann, Florian
Titel: Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing
Online-Publikationsdatum: 17-Aug-2023
Erscheinungsdatum: 2023
Sprache des Dokuments: Englisch
Zusammenfassung/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-Sachgruppe: 610 Medizin
610 Medical sciences
Veröffentlichende Institution: Johannes Gutenberg-Universität Mainz
Organisationseinheit: FB 04 Medizin
Veröffentlichungsort: Mainz
ROR: https://ror.org/023b0x485
DOI: http://doi.org/10.25358/openscience-9418
Version: Published version
Publikationstyp: Zeitschriftenaufsatz
Weitere Angaben zur Dokumentart: Scientific article
Nutzungsrechte: CC BY
Informationen zu den Nutzungsrechten: https://creativecommons.org/licenses/by/4.0/
Zeitschrift: Insights into imaging
14
Seitenzahl oder Artikelnummer: 47
Verlag: Springer
Verlagsort: Berlin u.a.
Erscheinungsdatum: 2023
ISSN: 1869-4101
DOI der Originalveröffentlichung: 10.1186/s13244-023-01392-y
Enthalten in den Sammlungen:DFG-491381577-G

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