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Classification of high-resolution manometry data according to videofluoroscopic parameters using pattern recognition.

Matthew R Hoffman1, Corinne A Jones, Zhixian Geng

  • 1Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin 53792, USA.

Otolaryngology--Head and Neck Surgery : Official Journal of American Academy of Otolaryngology-Head and Neck Surgery
|June 4, 2013
PubMed
Summary

Pattern recognition in high-resolution manometry (HRM) accurately identifies disordered swallowing features, potentially replacing traditional videofluoroscopy. This method offers quantitative, radiation-free data for bedside swallowing assessments.

Keywords:
MBSImPhigh-resolution manometrypharyngeal swallowvideofluoroscopy

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Area of Science:

  • Deglutition science
  • Medical imaging analysis
  • Artificial intelligence in healthcare

Background:

  • Swallowing disorders (dysphagia) significantly impact patient quality of life.
  • Videofluoroscopy (VF) is the traditional method for assessing pharyngeal swallow function.
  • The Modified Barium Swallow Impairment Profile (MBSImP) provides a standardized framework for VF analysis.

Purpose of the Study:

  • To evaluate pattern recognition techniques applied to high-resolution manometry (HRM) spatiotemporal plots.
  • To determine if HRM data can identify specific features of disordered swallowing as defined by the MBSImP.
  • To assess the potential of HRM as a radiation-free alternative to VF for swallowing assessment.

Main Methods:

  • A case series involving 30 subjects with dysphagia undergoing simultaneous HRM and videofluoroscopy.
  • Analysis of HRM spatiotemporal plots using a novel pattern recognition program with a multilayer perceptron artificial neural network (ANN).
  • Comparison of ANN-identified pharyngeal swallow components and penetration/aspiration status with MBSImP scores from videofluoroscopic studies.

Main Results:

  • Pattern recognition correctly identified normal or disordered MBSImP parameters with an average accuracy of approximately 91% (AUC 0.902-0.981).
  • Incorporating two MBSImP parameters into the classification model improved accuracy to over 93% (AUC 0.963-0.989).
  • The ANN successfully identified key pharyngeal swallowing components and penetration/aspiration from HRM data alone.

Conclusions:

  • Pattern recognition and multiparameter analysis of HRM spatiotemporal plots can effectively identify swallowing abnormalities.
  • HRM offers a quantitative, functional, and radiation-free method for assessing swallowing function at the bedside.
  • HRM shows promise in supplementing or potentially replacing traditional videofluoroscopic swallowing studies.