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Deep Learning for Classification of Pediatric Otitis Media.

Zebin Wu1,2, Zheqi Lin3, Lan Li2

  • 1Department of Otolaryngology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.

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|December 28, 2020
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Summary

This study developed an AI tool using CNNs to automatically classify pediatric otitis media (OM) from otoscope images. The AI shows high accuracy, enabling potential home monitoring by parents.

Keywords:
Deep learningartificial intelligencediagnosisotoscopesmartphone

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Otolaryngology

Background:

  • Pediatric otitis media (OM) diagnosis relies on visual inspection, which can be subjective.
  • Developing objective and accessible monitoring strategies is crucial for effective pediatric ear health management.

Purpose of the Study:

  • To create a novel, automated method for monitoring pediatric otitis media (OM) using convolutional neural networks (CNNs).
  • To develop a reliable and objective classification system for diagnosing acute otitis media (AOM) and otitis media with effusion (OME).

Main Methods:

  • Developed an otoscopic image classifier using deep learning (Xception and MobileNet-V2 architectures).
  • Trained and tested models on over 10,000 otoscopic images, including AOM, OME, and normal ear images.
  • Validated the models using a prospective test set of 102 smartphone-captured otoscope images for home screening assessment.

Main Results:

  • Achieved high accuracies for automated OM classification: Xception (97.45%) and MobileNet-V2 (95.72%) on the test set.
  • Smartphone-based classification showed promising results: Xception (90.66%) and MobileNet-V2 (88.56%).
  • Class activation maps confirmed that AI models extracted similar features from both standard and smartphone otoscopic images.

Conclusions:

  • Successfully developed deep learning algorithms for automated classification of pediatric AOM and OME from otoscopic images.
  • AI-powered, smartphone-enabled otoscopes offer potential for home-based early detection and continuous monitoring of pediatric ear infections.
  • This technology may reduce the frequency of clinical visits for managing pediatric otitis media.