Comparison of diagnostic performance of a deep learning algorithm, emergency physicians, junior radiologists and senior radiologists in the detection of appendicular fractures in children.

Journal: Pediatric radiology
Published Date:

Abstract

BACKGROUND: Advances have been made in the use of artificial intelligence (AI) in the field of diagnostic imaging, particularly in the detection of fractures on conventional radiographs. Studies looking at the detection of fractures in the pediatric population are few. The anatomical variations and evolution according to the child's age require specific studies of this population. Failure to diagnose fractures early in children may lead to serious consequences for growth.

Authors

  • Idriss Gasmi
    Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France.
  • Arvin Calinghen
    Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France.
  • Jean-Jacques Parienti
    GRAM 2.0 EA2656 UNICAEN Normandie, University Hospital, Caen, France.
  • Frederique Belloy
    Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France.
  • Audrey Fohlen
    Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France.
  • Jean-Pierre Pelage
    Department of Radiology, Caen University Medical Center, 14033 Cedex 9, Caen, France. pelage-jp@chu-caen.fr.