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

alexandrossfakianakis shared this article with you from Inoreader Predictive Outcomes of Deep Learning Measurement of the Anterior Glottic Angle in Bilateral Vocal Fold Immobility via The Laryngoscope The objective of this research was to apply a computer vision tool for assessment of anterior glottic angle (AGA) in patients with bilateral vocal fold immobility (BVFI), and to compare the AGA in BVFI with that of unilateral vocal fold immobility (UVFI) and normal larynges (NL) as measured by the algorithm. The computer vision tool was able to quantitatively assessof the AGA from videolaryngoscopy, demonstrating ability to discriminate between patients with BVFI, UVFI, and normal controls, as well as to 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. 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, 2022 View on Web

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