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Open Access 01-12-2023 | Artificial Intelligence | Original Article

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

Authors: Tobias Jorg, Benedikt Kämpgen, Dennis Feiler, Lukas Müller, Christoph Düber, Peter Mildenberger, Florian Jungmann

Published in: Insights into Imaging | Issue 1/2023

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.

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Title: Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing
Authors: Tobias Jorg
Benedikt Kämpgen
Dennis Feiler
Lukas Müller
Christoph Düber
Peter Mildenberger
Florian Jungmann
Publication date: 01-12-2023
Publisher: Springer Vienna
Keywords: Artificial Intelligence
Urolithiasis
Published in: Insights into Imaging / Issue 1/2023
Electronic ISSN: 1869-4101
DOI: https://doi.org/10.1186/s13244-023-01392-y

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Background

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Results

Conclusion

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