Ovarian torsion: developing a machine-learned algorithm for diagnosis.

Journal: Pediatric radiology
Published Date:

Abstract

BACKGROUND: Ovarian torsion is a common concern in girls presenting to emergency care with pelvic or abdominal pain. The diagnosis is challenging to make accurately and quickly, relying on a combination of physical exam, history and radiologic evaluation. Failure to establish the diagnosis in a timely fashion can result in irreversible ovarian ischemia with implications for future fertility. Ultrasound is the mainstay of evaluation for ovarian torsion in the pediatric population. However, even with a high index of suspicion, imaging features are not pathognomonic.

Authors

  • Jeffrey P Otjen
    Department of Radiology, Seattle Children's Hospital and the University of Washington, Seattle Children's Hospital, MA.7.220, 4800 Sand Point Way NE, Seattle, WA, 98105, USA. jeffrey.otjen@seattlechildrens.org.
  • A Luana Stanescu
    Department of Radiology, Seattle Children's Hospital and the University of Washington, Seattle Children's Hospital, MA.7.220, 4800 Sand Point Way NE, Seattle, WA, 98105, USA.
  • Adam M Alessio
    Computational Mathematics, Science, and Engineering (CMSE), Biomedical Engineering (BME) and Radiology, Institute for Quantitative Health Science & Engineering (IQ), Michigan State University, East Lansing, MI, USA.
  • Marguerite T Parisi
    Department of Radiology, Seattle Children's Hospital and the University of Washington, Seattle Children's Hospital, MA.7.220, 4800 Sand Point Way NE, Seattle, WA, 98105, USA.