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Automated multi-class classification for prediction of tympanic membrane changes with deep learning models.

Yeonjoo Choi1, Jihye Chae2, Keunwoo Park2

  • 1Department of Otorhinolaryngology-Head and Neck Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.

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Summary

Deep learning accurately classifies tympanic membrane (TM) conditions, even with multiple concurrent diseases. This AI tool supports clinical decisions by providing real-time, reproducible TM evaluations.

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Area of Science:

  • Otolaryngology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Tympanic membrane (TM) evaluation is crucial in clinical practice.
  • TM lesions often have multiple diagnostic names, complicating accurate diagnosis.
  • Investigating AI's role in classifying complex TM conditions is essential.

Purpose of the Study:

  • To assess the impact of concurrent diseases on deep learning classification performance for TM images.
  • To develop and evaluate a deep learning model for multi-class TM disease identification.

Main Methods:

  • A retrospective study utilizing a database of otoendoscopic images.
  • A customized EfficientNet-B4 architecture was employed for multi-class classification.
  • The model predicted primary classes (otitis media with effusion, chronic otitis media, none) and secondary classes (cholesteatoma, myringitis, otomycosis, ventilating tube).

Main Results:

  • High accuracy in primary class prediction (DSC 95.19%), with rare misidentification between OME and COM.
  • Strong performance in secondary class diagnosis (attic cholesteatoma 88.37%, myringitis 88.28%).
  • Minimal impact of concurrent diseases on overall prediction accuracy (0.44% error for multiple secondary classes).

Conclusions:

  • Deep learning models can accurately and reproducibly classify TM changes in real-time.
  • AI-powered classification supports clinical decision-making for TM conditions, including those with concurrent diseases.