Machine-learning-assisted Preoperative Prediction of Pediatric Appendicitis Severity.

Journal: Journal of pediatric surgery
Published Date: Jan 13, 2025

Abstract

PURPOSE: This study evaluates the effectiveness of machine learning (ML) algorithms for improving the preoperative diagnosis of acute appendicitis in children, focusing on the accurate prediction of the severity of disease.

Authors

  • Aylin Erman
    Department of Computer Science, McGill University, Montreal, QC, Canada. Electronic address: aylin.erman@mail.mcgill.ca.
  • Julia Ferreira
    Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Qc, Canada.
  • Waseem Abu Ashour
    Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Qc, Canada.
  • Elena Guadagno
    Harvey E. Beardmore Division of Pediatric Surgery, Montreal Children's Hospital, McGill University Health Center and McGill University, Montreal, Canada. Electronic address: elena.guadagno@muhc.mcgill.ca.
  • Etienne St-Louis
    McGill University Faculty of Medicine and Health Sciences, Canada; Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Qc, Canada.
  • Sherif Emil
    McGill University Faculty of Medicine and Health Sciences, Canada; Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Qc, Canada.
  • Jackie Cheung
    Department of Computer Science, McGill University, Montreal, QC, Canada; Canada CIFAR AI Chair, Mila, Canada.
  • Dan Poenaru
    Institute of Health Sciences Education, McGill University, Montreal, Quebec, Canada; Department of Pediatric Surgery, McGill University, Canada.

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