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Updated: Aug 28, 2025

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
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Radiology Text Analysis System (RadText): Architecture and Evaluation.

Song Wang1, Mingquan Lin2, Ying Ding3

  • 1Cockrell School of Engineering, The University of Texas at Austin, Austin, USA.

Proceedings. IEEE International Conference on Healthcare Informatics
|September 21, 2022
PubMed
Summary
This summary is machine-generated.

RadText is a new Python system for automated radiology report analysis, improving efficiency and accuracy in diagnosis. It offers advanced text processing for better data privacy and research integration.

Keywords:
Natural Language ProcessingRadiologyText Analysis Systems

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Radiology

Background:

  • Radiology report analysis is time-consuming and prone to errors.
  • There is a need for automated systems to support radiologists and improve diagnostic accuracy.

Purpose of the Study:

  • To introduce RadText, a high-performance, open-source Python system for automated radiology text analysis.
  • To provide an efficient and user-friendly pipeline for processing radiology reports.

Main Methods:

  • RadText employs a hybrid text processing schema supporting raw and local processing.
  • The system includes de-identification, section segmentation, tokenization, named entity recognition, parsing, and negation detection.
  • It uses BioC as a unified interface and standardizes output to the OMOP Common Data Model.

Main Results:

  • RadText achieved high performance on the MIMIC-CXR dataset with an average precision of 0.91, recall of 0.94, and F-1 score of 0.92.
  • The system demonstrated superior accuracy, usability, and data privacy compared to existing toolkits.
  • Annotated a test set for five new disease labels to support future research.

Conclusions:

  • RadText offers an efficient and accurate solution for automated radiology report analysis.
  • Its compatibility with OMOP CDM facilitates systematic observational research.
  • The open-source availability promotes wider adoption and further development in the field.