A multimodal machine learning algorithm improved diagnostic accuracy for otitis media in a school aged Aboriginal population.

Journal: Journal of biomedical informatics
PMID: 39971225

Abstract

OBJECTIVE: Otitis Media (OM) - ear infection - can lead to hearing loss and associated developmental delay. There are several subgroups of OM which can be difficult to diagnose accurately, even for experienced clinicians. AI and machine learning algorithms for OM diagnosis are evolving but typically only focus on one defined diagnostic feature of OM. This study aimed to establish if combining otoscopic and tympanometry data improves the diagnostic accuracy of a ML algorithm for diagnosing OM and its various subgroups.

Authors

  • Jacqueline H Stephens
    Flinders University, College of Medicine and Public Health, Flinders Health and Medical Research Institute, Adelaide, Australia. Electronic address: Jacqueline.stephens@flinders.edu.au.
  • Phong Phu Nguyen
    Flinders University, College of Science and Engineering, Adelaide, Australia.
  • Amanda Machell
    Flinders University, College of Medicine and Public Health, Flinders Health and Medical Research Institute, Adelaide, Australia. Electronic address: https://twitter.com/WatsonAmanda1.
  • Linnett Sanchez
    Flinders University, College of Nursing & Health Sciences, Adelaide, Australia.
  • Eng H Ooi
    Flinders University, College of Medicine and Public Health, Flinders Health and Medical Research Institute, Adelaide, Australia; Otolaryngology Head and Neck Surgery Unit, Flinders Medical Centre, Adelaide, Australia.
  • A Simon Carney
    Flinders University, College of Medicine and Public Health, Flinders Health and Medical Research Institute, Adelaide, Australia. Electronic address: https://twitter.com/carney_simon.
  • Trent Lewis
    Flinders University, College of Science and Engineering, Adelaide, Australia. Electronic address: https://twitter.com/trentwlewis.

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