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Embedded AI-Assisted Otoscopic Image Screening for Pediatric Otitis Media.

Changwei Lv1, Desheng Jia2, Zebin Wu2

  • 1Sino-German School of Robotics, Shenzhen University of Information Technology, China.

Ear, Nose, & Throat Journal
|April 11, 2026
PubMed
Summary

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An embedded artificial intelligence (AI) system for pediatric otitis media screening is feasible on a microcontroller. This AI tool can aid in point-of-care prediagnostic triage, supporting clinical workflows without replacing clinician diagnosis.

Area of Science:

  • Medical technology
  • Artificial intelligence in healthcare
  • Otolaryngology

Background:

  • Otitis media is a common pediatric condition requiring timely diagnosis.
  • Current diagnostic methods may not be readily available at the point-of-care.
  • Artificial intelligence (AI) offers potential for automated image analysis.

Purpose of the Study:

  • To evaluate an embedded AI system for otoscopic image analysis.
  • To assess AI performance for screening and prediagnostic triage of pediatric otitis media.
  • To determine feasibility of deploying AI on a microcontroller for point-of-care use.

Main Methods:

  • Retrospective analysis of 19,522 tympanic membrane images.
  • Training a lightweight convolutional neural network (AlexNet derivative).
Keywords:
deep learningembedded AIotitis mediaotoscopypediatrictympanic membrane

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  • Deployment of INT8 quantized AI models on an STM32H7 microcontroller for performance evaluation.

    • Main Results:

    • Full-precision AI model achieved 97.67% accuracy.
    • INT8 quantization maintained high accuracy (97.50%-97.67%).
    • Quantization reduced memory usage and inference latency on the microcontroller.

    Conclusions:

    • On-device otoscopic image analysis is feasible on microcontrollers.
    • Embedded AI can support otitis media screening and triage workflows.
    • The AI tool is intended for clinical support, not final diagnosis or treatment direction.