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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Deep learning to convert unstructured CT pulmonary angiography reports into structured reports.

Adam Spandorfer1, Cody Branch1, Puneet Sharma2

  • 1Department of Radiology, Medical University of South Carolina, 171 Ashley Avenue, Charleston, SC, 29425, USA.

European Radiology Experimental
|September 25, 2019
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A machine learning algorithm accurately converts unstructured computed tomography pulmonary angiography (CTPA) reports into structured reports, improving radiologist-clinician communication without sacrificing workflow efficiency.

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Artificial intelligenceMachine learningNatural language processingStructured reportingTomography (x-ray, computed)

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Area of Science:

  • Radiology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Structured reports enhance radiologist-provider communication.
  • Concerns exist regarding potential workflow efficiency decreases with structured reporting.
  • Machine learning offers a potential solution for efficient structured report generation.

Purpose of the Study:

  • To evaluate a machine learning algorithm for converting unstructured CTPA reports to structured reports.
  • To assess the impact of this algorithm on workflow efficiency and data quality.
  • To determine the accuracy of the algorithm in classifying report statements.

Main Methods:

  • A self-supervised convolutional neural network was trained on 475 structured CTPA reports.
  • The algorithm was applied to 400 unstructured CTPA reports.
  • Accuracy was assessed using strict and modified criteria by two independent observers.

Main Results:

  • The algorithm achieved high accuracy, correctly labeling 91.6% (strict criteria) and 95.9% (modified criteria) of 4,157 statements.
  • A small percentage of statements (6.6%) were problematic for labeling.
  • The majority of problematic statements included content for multiple categories.

Conclusions:

  • The machine learning algorithm demonstrates high accuracy in structuring free-text CTPA findings.
  • This technology can improve radiologist-clinician communication and data mining capabilities.
  • The algorithm shows potential for enhancing productivity in radiology departments.