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Related Concept Videos

Assessing Body Temperature - Tympanic membrane01:14

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Artificial intelligence to detect tympanic membrane perforations.

A-R Habib1,2, E Wong1, R Sacks3,4

  • 1Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Sydney, Australia.

The Journal of Laryngology and Otology
|April 3, 2020
PubMed
Summary
This summary is machine-generated.

An artificial intelligence algorithm shows promise in detecting tympanic membrane perforations from otoscopic images. This AI tool could aid in ear disease screening in remote areas.

Keywords:
EarMachine LearningOtoscopyTympanic Membrane

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Otolaryngology

Background:

  • Tympanic membrane perforations are common ear conditions.
  • Access to specialized otolaryngology care is limited in rural settings.
  • Novel diagnostic tools are needed for remote ear disease screening.

Purpose of the Study:

  • To assess the feasibility of an AI algorithm for detecting tympanic membrane perforations.
  • To develop a proof-of-concept tool for under-resourced areas.
  • To evaluate AI performance using otoscopic images.

Main Methods:

  • Retrospective analysis of 233 otoscopic images.
  • Transfer learning with Google's Inception-V3 convolutional neural network.
  • Ground truth established by otolaryngologists; perforation size categorized.

Main Results:

  • The AI algorithm achieved 76.0% overall accuracy in identifying intact and perforated tympanic membranes.
  • Area under the curve (AUC) was 0.867, indicating good diagnostic ability.
  • The model demonstrated capability in classifying perforation sizes.

Conclusions:

  • A proof-of-concept AI algorithm can detect tympanic membrane perforations.
  • Further development could lead to a valuable ear disease screening tool.
  • Future work aims to create a point-of-care tool for healthcare workers in remote locations.