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Automatic classification and prioritisation of actionable BI-RADS categories using natural language processing

P López-Úbeda1, T Martín-Noguerol2, A Luna2

  • 1NLP Department, HT Médica, C. Carmelo Torres 2, 23007 Jaén, Spain.

Clinical Radiology
|October 14, 2023
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Summary
This summary is machine-generated.

A new natural language processing (NLP) system accurately classifies Breast Imaging Reporting Data System (BI-RADS) categories in radiology reports. This tool enhances communication of critical findings to physicians, improving efficiency in breast imaging evaluations.

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Area of Science:

  • Radiology and Medical Imaging
  • Natural Language Processing (NLP)
  • Artificial Intelligence in Healthcare

Background:

  • Radiologists face routine tasks in evaluating breast radiology reports.
  • Efficient communication of Breast Imaging Reporting Data System (BI-RADS) categories is crucial for patient care.
  • Current workflows can be time-consuming, impacting timely decision-making.

Purpose of the Study:

  • To develop and evaluate a NLP-based system for classifying and prioritizing BI-RADS categories in breast imaging reports.
  • To enhance communication of relevant results to referring physicians.
  • To streamline radiologists' routine tasks in breast radiology evaluations.

Main Methods:

  • Developed an NLP system to classify BI-RADS categories (0-6) and prioritize reports (high vs. low priority).
  • Utilized three Bidirectional Encoder Representations from Transformers (BERT)-based models: XLM-RoBERTa, BETO, and Bio-BERT-Spanish.
  • Trained and tested models on distinct corpora of breast ultrasound and mammogram reports.

Main Results:

  • Achieved 74.29-77.5% accuracy in detecting BI-RADS categories.
  • Demonstrated 88.52-91.02% accuracy in prioritizing reports.
  • The system effectively classifies all BI-RADS categories within a single report.

Conclusions:

  • The NLP system can effectively classify BI-RADS categories and prioritize reports.
  • This automated tool assists radiologists in report evaluation and decision-making.
  • Enhances the speed of communicating priority BI-RADS reports to referring physicians.