Artificial intelligence improves resident detection of pediatric and young adult upper extremity fractures.

Journal: Skeletal radiology
Published Date: May 2, 2024

Abstract

PURPOSE: We wished to evaluate if an open-source artificial intelligence (AI) algorithm ( https://www.childfx.com ) could improve performance of (1) subspecialized musculoskeletal radiologists, (2) radiology residents, and (3) pediatric residents in detecting pediatric and young adult upper extremity fractures.

Authors

  • John R Zech
    Department of Medicine, California Pacific Medical Center, San Francisco, California, United States of America.
  • Chimere O Ezuma
    Department of Orthopedic Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Shreya Patel
    Department of Radiology, New York University Langone Health, 301 E 17th St, New York, NY, 10003, USA.
  • Collin R Edwards
    Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
  • Russell Posner
    University of Connecticut School of Medicine, 263 Farmington Ave. Farmington, CT 06030, USA.
  • Erin Hannon
    Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA.
  • Faith Williams
    Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA.
  • Sonali V Lala
    Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
  • Zohaib Y Ahmad
    Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
  • Matthew P Moy
    Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
  • Tony T Wong
    Department of Radiology, Columbia University Irving Medical Center, 622 West 168th St., MC 28, New York, NY, 10032, USA.

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