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Related Experiment Video

Updated: Oct 15, 2025

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing
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Deep learning-based artificial intelligence model for identifying swallow types in esophageal high-resolution

Wenjun Kou1, Galal Osama Galal2, Matthew William Klug2

  • 1Gastroenterology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.

Neurogastroenterology and Motility
|October 28, 2021
PubMed
Summary

A new artificial intelligence (AI) model accurately classifies esophageal swallow types using high-resolution manometry (HRM) data. This deep learning approach aids in the automated interpretation of swallowing disorders.

Keywords:
esophageal peristalsishigh-resolution manometrymachine learning

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Area of Science:

  • Gastroenterology
  • Medical AI
  • Swallowing Disorders

Background:

  • Esophageal high-resolution manometry (HRM) generates complex data for diagnosing swallowing issues.
  • Accurate classification of swallow types is crucial for patient management.
  • Current interpretation of HRM data can be time-consuming and subjective.

Purpose of the Study:

  • To develop and validate a deep learning, artificial intelligence (AI) model for automatic swallow type classification.
  • To assess the model's ability to classify peristalsis from HRM data.
  • To explore the potential of AI in streamlining HRM data interpretation.

Main Methods:

  • A dataset of 1,741 esophageal HRM studies (26,115 swallows) was collected and labeled according to the Chicago Classification.
  • A Long Short-Term Memory (LSTM) deep learning model was trained and evaluated on the dataset.
  • The model's performance was analyzed for individual swallow types and overall peristalsis classification.

Main Results:

  • The LSTM model achieved high accuracies: 0.86 (train), 0.81 (validation), and 0.83 (test) for swallow type classification.
  • The model demonstrated an accuracy of 0.88 for study-level peristalsis classification in the test dataset.
  • Most misclassifications were between adjacent categories, indicating generally reliable performance.

Conclusions:

  • A deep learning AI model can accurately and automatically classify swallow types and peristalsis from raw esophageal HRM data.
  • This AI model shows significant promise for improving the efficiency and accuracy of HRM study interpretation.
  • Further refinement and integration with broader manometric diagnoses are warranted for future clinical application.