A deep learning approach for detecting otitis media from wideband tympanometry measurements

Josefine Vilsbøll Sundgaard, Peter Bray, Søren Laugesen, James Harte, Yosuke Kamide, Chiemi Tanaka, Anders Nymark Christensen, Rasmus Reinhold Paulsen

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Objective: In this study, we propose an automatic diagnostic algorithm for detecting otitis media based on wideband tympanometry measurements. Methods: We develop a convolutional neural network for classification of otitis media based on the analysis of the wideband tympanogram. Saliency maps are computed to gain insight into the decision process of the convolutional neural network. Finally, we attempt to distinguish between otitis media with effusion and acute otitis media, a clinical subclassification important for the choice of treatment. Results: The approach shows high performance on the overall otitis media detection with an accuracy of 92.6%. However, the approach is not able to distinguish between specific types of otitis media. Conclusion: Out approach can detect otitis media with high accuracy and the wideband tympanogram holds more diagnostic information than the commonly used techniques wideband absorbance measurements and simple tympanograms. Significance: This study shows how advanced deep learning methods enable automatic diagnosis of otitis media based on wideband tympanometry measurements, which could become a valuable diagnostic tool.
Original languageEnglish
Article number9735386
JournalIEEE Journal of Biomedical and Health Informatics
Issue number7
Pages (from-to)2974 - 2982
Publication statusPublished - 2022


  • Ear
  • Media
  • Pressure measurement
  • Frequency measurement
  • Wideband
  • Irrigation
  • Biomedical measurement


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