Application of a Computer Vision Tool for Automated Glottic Tracking to Vocal Fold Paralysis Patients

Tiffany V. Wang, Nat Adamian, Phillip C. Song, Ramon A. Franco, Molly N. Huston, Nate Jowett, Matthew R. Naunheim

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Objectives: (1) Demonstrate true vocal fold (TVF) tracking software (AGATI [Automated Glottic Action Tracking by artificial Intelligence]) as a quantitative assessment of unilateral vocal fold paralysis (UVFP) in a large patient cohort. (2) Correlate patient-reported metrics with AGATI measurements of TVF anterior glottic angles, before and after procedural intervention. Study Design: Retrospective cohort study. Setting: Academic medical center. Methods: AGATI was used to analyze videolaryngoscopy from healthy adults (n = 72) and patients with UVFP (n = 70). Minimum, 3rd percentile, 97th percentile, and maximum anterior glottic angles (AGAs) were computed for each patient. In patients with UVFP, patient-reported outcomes (Voice Handicap Index 10, Dyspnea Index, and Eating Assessment Tool 10) were assessed, before and after procedural intervention (injection or medialization laryngoplasty). A receiver operating characteristic curve for the logistic fit of paralysis vs control group was used to determine AGA cutoff values for defining UVFP. Results: Mean (SD) 3rd percentile AGA (in degrees) was 2.67 (3.21) in control and 5.64 (5.42) in patients with UVFP (P <.001); mean (SD) 97th percentile AGA was 57.08 (11.14) in control and 42.59 (12.37) in patients with UVFP (P <.001). For patients with UVFP who underwent procedural intervention, the mean 97th percentile AGA decreased by 5 degrees from pre- to postprocedure (P =.026). The difference between the 97th and 3rd percentile AGA predicted UVFP with 77% sensitivity and 92% specificity (P <.0001). There was no correlation between AGA measurements and patient-reported outcome scores. Conclusions: AGATI demonstrated a difference in AGA measurements between paralysis and control patients. AGATI can predict UVFP with 77% sensitivity and 92% specificity.

Original languageEnglish
Pages (from-to)556-562
Number of pages7
JournalOtolaryngology - Head and Neck Surgery (United States)
Issue number4
StatePublished - Oct 2021


  • artificial intelligence
  • dysphonia
  • laryngology
  • laryngoscopy
  • patient-reported outcomes
  • vocal fold paralysis


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