Predictive Outcomes of Deep Learning Measurement of the Anterior Glottic Angle in Bilateral Vocal Fold Immobility.

OBJECTIVE: (1) To compare maximum glottic opening angle (anterior glottic angle, AGA) in patients with bilateral vocal fold immobility (BVFI), unilateral vocal fold immobility (UVFI) and normal larynges (NL), and (2) to correlate maximum AGA with patient-reported outcome measures. METHODS: Patients wisth BVFI, UVFI, and NL were retrospectively studied. An open-source deep learning-based computer vision tool for vocal fold tracking was used to analyze videolaryngoscopy. Minimum and maximum AGA were calculated and correlated with three patient-reported outcomes measures. RESULTS: Two hundred and fourteen patients were included. Mean maximum AGA was 29.91° (14.40° SD), 42.59° (12.37° SD), and 57.08° (11.14° SD) in BVFI (N = 70), UVFI (N = 70), and NL (N = 72) groups, respectively (p < 0.001). Patients requiring operative airway intervention for BVFI had an average maximum AGA of 24.94° (10.66° SD), statistically different from those not requiring intervention (p = 0.0001). There was moderate negative correlation between Dyspnea Index scores and AGA (Spearman r = -0.345, p = 0.0003). Maximum AGA demonstrated high discriminatory ability for BVFI diagnosis (AUC 0.92, 95% CI 0.81-0.97, p < 0.001) and moderate ability to predict need for operative airway intervention (AUC 0.77, 95% CI 0.64-0.89, p < 0.001). CONCLUSIONS: A computer vision tool for quantitative assessment of the AGA from videolaryngoscopy demonstrated ability to discriminate between patients with BVFI, UVFI, and normal controls and predict need for operative airway intervention. This tool may be useful for assessment of other neurological laryngeal conditions and may help guide decision-making in laryngeal surgery. LEVEL OF EVIDENCE: III Laryngoscope, 133:2285-2291, 2023.

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

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.

An Open-Source Computer Vision Tool for Automated Vocal Fold Tracking From Videoendoscopy.

OBJECTIVES: Contemporary clinical assessment of vocal fold adduction and abduction is qualitative and subjective. Herein is described a novel computer vision tool for automated quantitative tracking of vocal fold motion from videolaryngoscopy. The potential of this software as a diagnostic aid in unilateral vocal fold paralysis is demonstrated. STUDY DESIGN: Case-control. METHODS: A deep-learning algorithm was trained for vocal fold localization from videoendoscopy for automated frame-wise estimation of glottic opening angles. Algorithm accuracy was compared against manual expert markings. Maximum glottic opening angles between adults with normal movements (N = 20) and those with unilateral vocal fold paralysis (N = 20) were characterized. RESULTS: Algorithm angle estimations demonstrated a correlation coefficient of 0.97 (P < .001) and mean absolute difference of 3.72° (standard deviation [SD], 3.49°) in comparison to manual expert markings. In comparison to those with normal movements, patients with unilateral vocal fold paralysis demonstrated significantly lower maximal glottic opening angles (mean 68.75° ± 11.82° vs. 49.44° ± 10.42°; difference, 19.31°; 95% confidence interval [CI] [12.17°-26.44°]; P < .001). Maximum opening angle less than 58.65° predicted unilateral vocal fold paralysis with a sensitivity of 0.85 and specificity of 0.85, with an area under the receiver operating characteristic curve of 0.888 (95% CI [0.784-0.991]; P < .001). CONCLUSION: A user-friendly software tool for automated quantification of vocal fold movements from previously recorded videolaryngoscopy examinations is presented, termed automated glottic action tracking by artificial intelligence (AGATI). This tool may prove useful for diagnosis and outcomes tracking of vocal fold movement disorders. LEVEL OF EVIDENCE: IV Laryngoscope, 131:E219-E225, 2021.