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[Basics and applications of Natural Language Processing (NLP) in radiology]
F Jungmann1, S Kuhn2, B Kämpgen3
1Klinik und Poliklinik für Diagnostische und Interventionelle Radiologie, Universitätsmedizin, Johannes Gutenberg-Universität Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland. florian.jungmann@unimedizin-mainz.de.
Der Radiologe
|July 18, 2018
Summary
Natural Language Processing (NLP) is an automated tool for quality assurance in radiology. This study shows NLP effectively aids in the internal quality testing of radiology reports.
Area of Science:
- Medical Informatics
- Radiology
Background:
- Increasing demands in radiology necessitate quality assurance and process optimization tools.
- Automated applications are crucial for managing growing workloads in medical imaging.
Purpose of the Study:
- To explain the principles of Natural Language Processing (NLP) for structuring free-text radiology reports.
- To outline application scenarios for NLP in radiology.
Main Methods:
- Utilized linguistic techniques, statistical methods, and deep learning for text structuring.
- Employed medical ontologies like RadLex® for findings coding.
- Applied NLP to assess the quality of 3756 CT reports in a clinical setting.
Main Results:
- NLP demonstrated effectiveness as an automated tool for internal quality testing in radiology.
- The case study confirmed NLP's utility in evaluating report quality.
Conclusions:
- Natural Language Processing presents diverse applications for decision support in radiology.
- NLP facilitates enhanced quality management within radiology departments.
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