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Related Concept Videos

X-ray Imaging01:24

X-ray Imaging

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Generalized Radiographic View Identification with Deep Learning.

Xiang Fang1, Leah Harris2, Wei Zhou3

  • 1Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.

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|December 2, 2020
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Summary
This summary is machine-generated.

A machine learning algorithm using convolutional neural networks (CNNs) shows promise for automatically identifying radiographic views in diagnostic radiography. This AI system achieved acceptable accuracy, demonstrating feasibility for clinical quality control.

Keywords:
Artificial neural networkMachine learningQuality controlRadiography

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiography

Background:

  • Quality control in diagnostic radiography is crucial for accurate diagnoses.
  • Manual review of radiographic images is time-consuming and prone to human error.
  • Developing automated systems can improve efficiency and consistency in radiographic quality assessment.

Purpose of the Study:

  • To evaluate the feasibility of a machine learning algorithm for automated quality control in diagnostic radiography.
  • To assess the performance of a convolutional neural network (CNN)-based algorithm in identifying radiographic views at various classification levels.

Main Methods:

  • A retrospective study analyzed 15,046 radiographic images from nine clinical sites.
  • A CNN model (Inception V3) was trained using transfer learning to classify images across four levels: anatomy, laterality, projection, and detailed.
  • Performance metrics included sensitivity, positive predictive value, and overall accuracy, with and without "reasonable errors" considered.

Main Results:

  • The CNN algorithm achieved high overall accuracy: 0.96 (level 1), 0.93 (level 2), 0.90 (level 3), and 0.86 (level 4).
  • Accuracy improved to 0.99, 0.97, 0.94, and 0.88 when "reasonable errors" were permitted.
  • The model demonstrated acceptable performance in identifying radiographic views, particularly at lower classification levels.

Conclusions:

  • Machine learning algorithms, specifically CNNs, are feasible for automated quality control in diagnostic radiography.
  • The developed system can identify radiographic views with acceptable accuracy, supporting its potential clinical application.
  • Automated AI-driven quality control can enhance the efficiency and reliability of radiographic practices.