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External validation based on transfer learning for diagnosing atelectasis using portable chest X-rays.

Xiaxuan Huang1,2, Baige Li3, Tao Huang2

  • 1Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, China.

Frontiers in Medicine
|August 8, 2022
PubMed
Summary
This summary is machine-generated.

Transfer learning effectively detects atelectasis on portable chest X-rays, showing high accuracy in external validation. This method aids in diagnosing lung conditions using readily available imaging technology.

Keywords:
ICUsResNetartificial intelligence (AI)atelectasistransfer learning

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Limited research exists on classifying portable chest X-rays.
  • Atelectasis detection is crucial in intensive care settings.

Purpose of the Study:

  • To assess transfer learning feasibility for detecting atelectasis using portable chest X-rays.
  • To validate the model's performance on an external dataset.

Main Methods:

  • Utilized the MIMIC-CXR database (45,808 atelectasis, 75,455 no finding).
  • Extracted 60,000 images, categorized into 'normal' and 'atelectasis'.
  • Created an external dataset of 300 images from The First Affiliated Hospital of Jinan University.

Main Results:

  • Internal training achieved an AUC of 88.57% and accuracy of 81.70%.
  • External validation demonstrated a superior AUC of 98.39% and accuracy of 86.90%.

Conclusions:

  • Transfer learning models trained on large datasets show excellent external validation for atelectasis detection.
  • The model accurately localizes lesions on portable chest X-rays.