Automated Detection of Pediatric Foreign Body Aspiration from Chest X-rays Using Machine Learning.

Journal: The Laryngoscope
Published Date: Aug 1, 2024

Abstract

OBJECTIVE/HYPOTHESIS: Standard chest radiographs are a poor diagnostic tool for pediatric foreign body aspiration. Machine learning may improve upon the diagnostic capabilities of chest radiographs. The objective is to develop a machine learning algorithm that improves the diagnostic capabilities of chest radiographs in pediatric foreign body aspiration.

Authors

  • Brandon Truong
    School of Medicine, University of California, San Francisco, California, U.S.A.
  • Matthew Zapala
    Division of Pediatric Radiology, Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, U.S.A.
  • Bamidele Kammen
    Division of Pediatric Radiology, Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, U.S.A.
  • Kimberly Luu
    Division of Pediatric Otolaryngology, Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, California, U.S.A.

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