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Related Experiment Video

Updated: May 28, 2025

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RADEX: a rule-based clinical and radiology data extraction tool demonstrated on thyroid ultrasound reports.

Lewis Howell1,2, Amir Zarei3, Tze Min Wah3,4

  • 1School of Computing, University of Leeds, Leeds, LS2 9JT, UK. scljh@leeds.ac.uk.

European Radiology
|February 13, 2025
PubMed
Summary

A new rule-based tool, RADEX, automates information extraction from radiology reports. It efficiently classifies thyroid ultrasound findings, offering a time-saving solution for research and audits.

Keywords:
Data annotationInformation extractionNatural Language ProcessingThyroidUltrasound

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Area of Science:

  • Medical Informatics
  • Radiology
  • Natural Language Processing

Background:

  • Radiology reports contain valuable data for research, but it's often locked in unstructured free text.
  • Extracting this information manually is time-consuming and challenging, hindering secondary analyses and AI model training.

Purpose of the Study:

  • To develop and evaluate a rule-based tool, RADEX (RAdiology Data EXtraction), for automated information extraction from clinical documents.
  • To simplify the process of translating domain expertise into searchable models without requiring specialist Natural Language Processing (NLP) knowledge.

Main Methods:

  • RADEX was developed using a rule-based approach, converting domain expertise into regular-expression models.
  • Its utility was tested on a large dataset of 16,246 UK thyroid ultrasound reports for multi-label classification of 14 clinical features.
  • Performance was evaluated using a holdout test set against reference-standard labels.

Main Results:

  • RADEX achieved high performance in classifying thyroid ultrasound findings, with micro-average sensitivity of 0.97, specificity of 0.96, and F1-score of 0.94.
  • The tool demonstrated rapid processing at 12.3 milliseconds per report.
  • Inter-rater reliability for reference labels was high (Cohen's Kappa 0.94-0.95).

Conclusions:

  • RADEX is a versatile, time-saving, and freely available tool for extracting structured data from radiology reports.
  • It is particularly useful for research and audit applications with limited labeled data or computing resources, prioritizing explainability and reproducibility.
  • The tool accelerates data collection, enabling new insights and potentially improving patient care.