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Related Concept Videos

Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and the...

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Text Report Analysis to Identify Opportunities for Optimizing Target Selection for Chest Radiograph Artificial

Carl Sabottke1, Jason Lee2, Alan Chiang2

  • 1Department of Medical Imaging, University of Arizona College of Medicine, Tucson, AZ, USA. cfs121090@gmail.com.

Journal of Imaging Informatics in Medicine
|February 12, 2024
PubMed
Summary
This summary is machine-generated.

Analyzing chest radiograph (CXR) reports with natural language processing (NLP) identified key imaging findings impacting report length. This research aids in developing AI systems to enhance radiologist efficiency in interpreting complex CXR findings.

Keywords:
Artificial intelligence (AI)Chest radiographyNatural language processing (NLP)

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Area of Science:

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

Background:

  • Radiology report length and complexity can be influenced by various factors, including specific imaging findings.
  • Understanding these factors is crucial for optimizing radiologist workflow and developing assistive technologies.

Purpose of the Study:

  • To identify imaging findings in chest radiograph (CXR) reports that significantly impact report length and complexity.
  • To explore opportunities for designing AI systems that enhance radiologist efficiency.

Main Methods:

  • Retrospective analysis of 210,025 MIMIC-CXR reports and 168,949 local institutional reports (2019-2022).
  • Extraction of 59 imaging finding keywords using NLP.
  • Assessment of keyword impact on report length via linear regression (with and without LASSO regularization).
  • Analysis of additional factors like signing radiologist and terms of perception.

Main Results:

  • Imaging finding keywords explained a substantial portion of report word count (R²=0.469 local, R²=0.354 MIMIC-CXR).
  • Terms of perception had a lesser impact (R²=0.067 local, R²=0.086 MIMIC-CXR).
  • A combined model including radiologist, keywords, and perception terms yielded higher explanatory power (R²=0.570 local).
  • LASSO identified endotracheal tubes and pleural drains (local), and masses, nodules, cavitary/cystic lesions (MIMIC-CXR) as highly impactful.

Conclusions:

  • NLP and regression analysis effectively highlight imaging findings that influence radiology report characteristics.
  • These insights can guide the development of AI tools to improve radiologist efficiency in chest radiograph interpretation.
  • Targeting specific findings can lead to more efficient AI-assisted radiology workflows.