Intersegmenter Variability in High‐Speed Laryngoscopy‐Based Glottal Area Waveform Measures

Intersegmenter Variability in High‐Speed Laryngoscopy‐Based Glottal Area Waveform Measures:

Objectives/Hypothesis

High‐speed videoendoscopy (HSV) has potential to objectively quantify vibratory vocal fold characteristics during phonation. Glottal Analysis Tools (GAT) version 2018, developed in Erlangen, Germany, is software for determining various glottal area waveform (GAW) quantities. Before having GAT analyze HSV videos, segmenters have to define glottis manually across videos in a semiautomatic segmentation protocol. Such interventions are hypothesized to induce variability of subsequent GAW measure computation across segmenters and may attenuate GAT measures’ reliability to a certain point. This study explored intersegmenter variability in GAT's GAW measures based on semiautomatic image processing.

Study Design

Cohort study of rater reliability.

Methods

In total, 20 HSV videos from normophonic and dysphonic subjects with various laryngeal disorders were selected for this study and segmented by three trained segmenters. They separately segmented glottis areas in the same frame sets of the videos. Upon analysis of GAW, GAT offers 46 measures related to topologic GAW dynamic characteristics, GAW periodicity and perturbation characteristics, and GAW harmonic components. To address GAT's reliability, intersegmenter‐based variability in these measures was examined with intraclass correlation coefficient (ICC).

Results

In general, ICC behavior of the 46 GAW measures across three raters was highly acceptable. ICC of one parameter was moderate (0.5 < ICC < 0.75), good for seven parameters (0.75 < ICC < 0.9), and excellent for 38 parameters (0.9 < ICC).

Conclusions

Overall, high ICC values confirm clinical applicability of GAT for objective and quantitative assessment of HSV. Small intersegmenter differences with actual small parameter differences suggest that manual or semiautomatic segmentation in GAT does not noticeably influence clinical assessment outcome. To guarantee the software's performance, we suggest segmentation training before clinical application.

Level of Evidence

2b Laryngoscope, 2019