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Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for

Rami Saab1, Arjun Balachandar1, Hamza Mahdi1

  • 1Hurvitz Brain Sciences Program, Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.

Frontiers in Neuroscience
|December 22, 2023
PubMed
Summary

Deep learning models show promise for detecting post-stroke dysphagia using voice biomarkers. This automated screening method could improve early detection and patient outcomes for swallowing difficulties after a stroke.

Keywords:
Artificial Intelligencedysphagiamachine learningneural technologyoriginal research strokequality improvementstrokeswallowing

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Speech Pathology

Background:

  • Post-stroke dysphagia is a common complication, leading to increased morbidity and mortality.
  • Current bedside screening methods for dysphagia can be subjective and may limit patient access.
  • Voice changes are recognized as a potential indicator of dysphagia, offering a non-invasive biomarker.

Purpose of the Study:

  • To develop and evaluate a proof-of-concept deep learning model for automated dysphagia screening in post-stroke patients.
  • To assess the feasibility of using voice recordings as a biomarker for detecting dysphagia.

Main Methods:

  • A single-center study involving 68 post-stroke patients (40 training, 28 testing).
  • Voice data (vowels, words, sentences) were collected and processed into Mel-spectrogram images.
  • Deep learning models (DenseNet, ConvNext) were trained and tested on audio clip data for dysphagia classification.

Main Results:

  • Clip-level analysis showed a sensitivity of 71% and specificity of 77% (AUC = 0.80).
  • Participant-level analysis achieved higher accuracy with 89% sensitivity and 79% specificity (AUC = 0.91).

Conclusions:

  • This study demonstrates the feasibility of using deep learning on vocalizations for post-stroke dysphagia detection.
  • The findings suggest a potential for enhancing dysphagia screening through objective, voice-based biomarkers.