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Related Experiment Video

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Detection of Arytenoid Dislocation Using Pixel-valued Cuneiform Movement.

Ashley P O'Connell Ferster1, Michael C Ferster2, Haley Glatthorn3

  • 1Department of Surgery, Division of Otolaryngology-Head & Neck Surgery, Penn State Health: Milton S. Hershey Medical Center, Hershey, Pennsylvania.

Journal of Voice : Official Journal of the Voice Foundation
|February 4, 2018
PubMed
Summary

Pixel-valued movement software shows promise for detecting arytenoid dislocation, but high misdiagnosis rates limit its current clinical utility. Further refinement is needed for reliable preoperative diagnosis.

Keywords:
Arytenoid dislocationEndoscopyPixel-valued cuneiform movementVideolaryngoscopyVocal fold paralysis

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Area of Science:

  • Otolaryngology
  • Medical Imaging Analysis
  • Surgical Diagnostics

Background:

  • Arytenoid dislocation is a condition affecting voice production.
  • Accurate preoperative diagnosis is crucial for effective treatment planning.
  • Current diagnostic methods may have limitations in identifying subtle dislocations.

Purpose of the Study:

  • To evaluate the effectiveness of a specific pixel-valued movement software in the preoperative detection of arytenoid dislocation.
  • To assess the software's precision and reliability using retrospective endoscopic video analysis.

Main Methods:

  • Retrospective analysis of 27 patients with confirmed unilateral arytenoid dislocation.
  • Utilized region-tracking software to analyze cuneiform movement during inspiration from preoperative endoscopic videos.
  • Normalized movement data using vocal fold length and performed blinded measurements by two independent raters.

Main Results:

  • The software detected significantly greater cuneiform movement in nondislocated arytenoids compared to dislocated ones (P=0.001).
  • High interrater (0.75-0.87) and intrarater (0.87-0.91) correlation coefficients were observed.
  • Significant misdiagnosis of dislocation laterality occurred in 4-6 patients, and the area under the ROC curve was 0.63-0.90.

Conclusions:

  • The analyzed software demonstrates good precision and reliability based on correlation coefficients.
  • However, the software's current utility as a clinical diagnostic tool is limited due to high rates of misdiagnosis and a laborious analysis process.
  • Further development is necessary to improve accuracy and efficiency for clinical application.