Machine Learning for Accurate Intraoperative Pediatric Middle Ear Effusion Diagnosis.

Journal: Pediatrics
Published Date: Apr 1, 2021

Abstract

OBJECTIVES: Misdiagnosis of acute and chronic otitis media in children can result in significant consequences from either undertreatment or overtreatment. Our objective was to develop and train an artificial intelligence algorithm to accurately predict the presence of middle ear effusion in pediatric patients presenting to the operating room for myringotomy and tube placement.

Authors

  • Matthew G Crowson
    Department of Otolaryngology-Head and Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada.
  • Christopher J Hartnick
    Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, Massachusetts.
  • Gillian R Diercks
    Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, Massachusetts.
  • Thomas Q Gallagher
    Department of Otolaryngology-Head and Neck Surgery, Eastern Virginia Medical School, Norfolk, Virginia.
  • Mary S Fracchia
    Department of Pediatrics, Massachusetts General Hospital for Children, Boston, Massachusetts; and.
  • Jennifer Setlur
    Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, Massachusetts.
  • Michael S Cohen
    Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, Massachusetts.

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