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A Deep-Learning Diagnostic Support System for the Detection of COVID-19 Using Chest Radiographs: A Multireader

Matthias Fontanellaz1, Lukas Ebner2, Adrian Huber2

  • 1From the ARTORG Center for Biomedical Engineering Research, University of Bern.

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An artificial intelligence system demonstrated superior accuracy in detecting COVID-19 pneumonia on chest X-rays (CXRs) compared to human radiologists. This AI tool offers a promising advancement for diagnosing COVID-19 pneumonia, outperforming medical experts in accuracy and efficiency.

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Infectious Disease Diagnostics

Background:

  • Chest X-rays (CXRs) are crucial for diagnosing pneumonia, including COVID-19 pneumonia.
  • Human interpretation of CXRs can be subjective and vary in accuracy among radiologists.
  • Developing automated diagnostic tools is essential for efficient and accurate disease detection.

Purpose of the Study:

  • To evaluate the diagnostic performance of a novel artificial intelligence (AI) system for detecting COVID-19 pneumonia on CXRs.
  • To compare the AI system's accuracy against that of human radiologists with varying expertise levels.
  • To assess the AI system's effectiveness in differentiating COVID-19 pneumonia from other pneumonias and normal CXRs.

Main Methods:

  • Utilized five publicly available databases with normal CXRs, COVID-19 pneumonia, and other pneumonia cases.
  • Trained and tested an AI system on a harmonized dataset, including 100 cases per category in the testing set.
  • Compared the AI system's performance (sensitivity, specificity, PPV, F-score) against 11 blinded radiologists using statistical analysis (χ2 test).

Main Results:

  • The AI system achieved significantly higher overall diagnostic accuracy (94.3%) than radiologists (61.4% ± 5.3%).
  • AI demonstrated superior sensitivity for normal CXR (98.0%), other pneumonia (88.0%), and COVID-19 pneumonia (97.0%) compared to radiologists.
  • The AI system's F-score (94.3% ± 2.0%) was significantly higher than that of the radiologists (65.5% ± 12.4%), indicating better overall performance.

Conclusions:

  • The AI system exhibits robust accuracy in detecting COVID-19 pneumonia on CXRs.
  • The AI system surpasses the diagnostic performance of radiologists across different experience levels.
  • This AI-based approach represents a significant advancement for COVID-19 pneumonia diagnosis using chest radiography.