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A deep learning approach to dysphagia-aspiration detecting algorithm through pre- and post-swallowing voice changes.

Jung-Min Kim1,2, Min-Seop Kim3, Sun-Young Choi2

  • 1Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.

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Summary

A deep learning model effectively detects dysphagia-aspiration by analyzing pre- and post-swallowing voice changes. This voice analysis tool offers potential for real-time patient monitoring and personalized interventions in healthcare settings.

Keywords:
aspiration detection modeldeep learningdysphagia-aspirationvoice changes pre-and post-swallowingvoice-based non-face-to-face monitoring

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Speech Science

Background:

  • Dysphagia-aspiration poses significant health risks, often requiring invasive diagnostic methods.
  • Voice changes before and after swallowing are under-researched indicators of dysphagia-aspiration.
  • Weakened muscles and airway blockage in dysphagia patients may manifest as distinct voice alterations.

Purpose of the Study:

  • To develop and evaluate a deep learning model for identifying dysphagia-aspiration based on voice characteristics.
  • To analyze pre- and post-swallowing voice changes in patients with dysphagia compared to healthy individuals.
  • To explore the potential of voice analysis for non-invasive, real-time monitoring of dysphagia-aspiration.

Main Methods:

  • A prospective cohort study involving 198 participants (>40 years) was conducted.
  • Pre- and post-swallowing voice data were collected, processed into Mel spectrograms, and analyzed using a modified EfficientAT deep learning model (incorporating MobileNetV3).
  • The model underwent 10-fold cross-validation for robust performance evaluation, categorizing voices as normal or aspirated.

Main Results:

  • The machine-learning model demonstrated varying performance across sexes, with higher Area Under the Curve (AUC) values for males (0.8117–0.8319) than females (0.6975–0.7331).
  • Optimal model configurations included specific learning rates and batch sizes, differing between sexes.
  • A combined model balancing sex representation achieved AUC values of 0.7746–0.7997, indicating good overall detection capability.

Conclusions:

  • A voice analysis-based program shows promise for detecting pre- and post-swallowing changes associated with dysphagia-aspiration.
  • This technology could enable real-time monitoring of patient conditions.
  • The findings support the development of personalized interventions for dysphagia management.