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Multi-feature machine learning classification of sonotubometry for eustachian tube dysfunction assessment.

Linwei Zhang1, Xikun Lu2, Yangyang Zheng3

  • 1School of Computer Science and Technology, East China Normal University, Shanghai 200241, China.

Hearing Research
|November 23, 2025
PubMed
Summary

Machine learning accurately detects Eustachian tube dysfunction (ETD) using sonotubometry audio. This non-invasive method shows promise for diagnosing ETD, a key factor in childhood ear infections.

Keywords:
Eustachian tube dysfunctionMFCCMachine learningSonotubometry

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Area of Science:

  • Otolaryngology
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Eustachian tube dysfunction (ETD) is a primary cause of otitis media with effusion in children.
  • Current diagnostic methods for ETD lack a gold standard, and sonotubometry interpretation is challenging.
  • Sonotubometry assesses Eustachian tube function by measuring sound transmission during swallowing.

Purpose of the Study:

  • To develop and evaluate a machine learning model for objective detection and classification of ETD using sonotubometry.
  • To improve the diagnostic accuracy and efficiency of ETD assessment.
  • To identify key acoustic features indicative of normal Eustachian tube function.

Main Methods:

  • Audio features were extracted from sonotubometry recordings, with Mel-frequency cepstral coefficients (MFCC) identified as optimal.
  • A convolutional neural network (CNN) model was trained and validated using the extracted MFCC features.
  • Feature heatmaps were generated to understand the model's classification basis.

Main Results:

  • The MFCC-CNN model achieved a high sensitivity of 0.975 for ETD detection, significantly outperforming traditional methods (0.645).
  • Analysis revealed that acoustic responses between 6 to 8 kHz are crucial for classifying normal Eustachian tube opening.
  • The ML approach demonstrated superior performance compared to existing threshold-based diagnostic techniques.

Conclusions:

  • Machine learning-based sonotubometry offers a promising objective, non-invasive, and efficient diagnostic tool for Eustachian tube dysfunction.
  • This AI-driven approach can overcome the limitations of subjective interpretation in traditional sonotubometry.
  • The findings pave the way for improved diagnosis and management of ETD in pediatric populations.