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Related Experiment Video

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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Automatic Lung-RADS™ classification with a natural language processing system.

Sebastian E Beyer1, Brady J McKee1, Shawn M Regis2

  • 1Department of Radiology, Lahey Hospital and Medical Center, Burlington, MA, USA.

Journal of Thoracic Disease
|December 10, 2017
PubMed
Summary
This summary is machine-generated.

A natural language processing (NLP) algorithm accurately suggests Lung-RADS™ (LR) categories from CT lung screening reports. Standardized reporting improves NLP accuracy for lung nodule characterization.

Keywords:
CT lung screening (CTLS)Lung-RADS™ (LR)natural language processing (NLP)

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Lung nodules detected via CT lung screening (CTLS) require categorization using Lung Imaging Reporting and Data System (Lung-RADS™).
  • Accurate categorization is crucial for patient management and reducing unnecessary procedures.
  • Standardized reporting in CTLS can enhance the consistency and reliability of findings.

Purpose of the Study:

  • To develop and evaluate a natural language processing (NLP) algorithm for automated Lung-RADS™ (LR) category assignment.
  • To assess the algorithm's ability to extract imaging characteristics of lung nodules from structured CT reports.
  • To determine the impact of standardized reporting language on NLP performance.

Main Methods:

  • Trained an NLP algorithm on a dataset of structured CT lung screening (CTLS) reports.
  • Utilized a dataset of 1421 CTLS exams from an ACR accredited center.
  • Compared NLP-assigned LR categories against radiologist-assigned categories (gold standard).

Main Results:

  • The NLP algorithm achieved a sensitivity of 74.1% and specificity of 98.6% for identifying suspicious nodules (LR 4).
  • For positive nodules (LR 3/4), sensitivity was 75.0% and specificity was 98.8%.
  • In a subgroup with standardized language, sensitivity for LR 4 nodules increased to 87.0% with 99.5% specificity.

Conclusions:

  • An NLP system can effectively suggest appropriate Lung-RADS™ categories from CTLS reports.
  • Standardized reporting language significantly enhances the accuracy of NLP-driven LR categorization.
  • NLP holds potential for improving efficiency and consistency in lung nodule management.