Current and emerging artificial intelligence applications for pediatric abdominal imaging.

Journal: Pediatric radiology
Published Date:

Abstract

Artificial intelligence (AI) uses computers to mimic cognitive functions of the human brain, allowing inferences to be made from generally large datasets. Traditional machine learning (e.g., decision tree analysis, support vector machines) and deep learning (e.g., convolutional neural networks) are two commonly employed AI approaches both outside and within the field of medicine. Such techniques can be used to evaluate medical images for the purposes of automated detection and segmentation, classification tasks (including diagnosis, lesion or tissue characterization, and prediction), and image reconstruction. In this review article we highlight recent literature describing current and emerging AI methods applied to abdominal imaging (e.g., CT, MRI and US) and suggest potential future applications of AI in the pediatric population.

Authors

  • Jonathan R Dillman
    Department of Radiology, Division of Thoracoabdominal Imaging, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Ave., Cincinnati, OH, 45229-3039, USA. jonathan.dillman@cchmc.org.
  • Elan Somasundaram
    Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA.
  • Samuel L Brady
    Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH 45229.
  • Lili He
    Department of Food Science, University of Massachusetts Amherst, United States of America. Electronic address: lilihe@foodsci.umass.edu.