Laryngeal High-speed Videoendoscopy Research Articles

Supraglottic Laryngeal Maneuvers in Adductor Laryngeal Dystonia During Connected Speech

ObjectiveAdductor laryngeal dystonia (AdLD) disrupts fine motor movements of vocal folds during speech, resulting in a strained, broken, and strangled voice. Laryngeal high-speed videoendoscopy (HSV) in connected speech enables the direct visualization of detailed laryngeal dynamics, hence, it can be effectively used to study AdLD. The current study utilizes HSV to investigate supraglottic laryngeal tissue maneuvers obstructing the view of the vocal folds, in AdLD and normophonic speakers during connected speech. Characterizing the laryngeal maneuvers in these groups can facilitate a deeper understanding of the normophonic voice physiology and AdLD voice pathophysiology. MethodsHSV data were obtained from six normophonic speakers and six patients with AdLD during production of connected speech. Three experienced raters visually analyzed the data to determine laryngeal tissues leading to obstructions of vocal folds in HSV images. The raters recorded the duration of each obstruction and indicated the specific tissue(s) leading to the obstruction After the completion of their individual visual analysis, the raters came to consensus about their observations and measurements. ResultsStatistical analysis indicated that AdLD patients exhibited higher occurrences of vocal fold obstructions and longer durations of obstructions compared to the normophonic group. Similar obstruction types were found in both groups, with the epiglottis being the primary site of obstruction for both. Participants with AdLD displayed significantly elevated occurrences of sphincteric compression resulting in vocal fold obstruction. ConclusionHSV can be used to study the movements of laryngeal tissues in detail during connected speech. The analysis of supraglottic laryngeal tissue dynamics in speech can help us characterize the AdLD pathophysiology. The study's findings regarding the tissues implicated in obstructions may potentially inform the development of patient-specific therapeutic strategies targeting individual control over specific laryngeal muscles during phonation and speech production.

Preliminary Findings of Vocal Fold Vibratory Characteristics of Singers Analyzed by Laryngeal High-Speed Videoendoscopy

ObjectivesThis study investigates the vocal fold vibratory dynamics of singers, which are postulated to differ from those of normal speakers due to the singers' regular vocal training. The measurement of vocal fold vibration involved the utilization of laryngeal high-speed video endoscopy (LHSV) and subsequent LHSV-based analysis. The focus is to characterize and compare the LHSV-based measures derived from the glottal area waveform (GAW), namely fundamental frequency (F0GAW), glottal perturbation (jitterGAW and shimmerGAW), open quotient (OQGAW), and Nyquist plots, between singers and normal speakers across genders. MethodsParticipants comprised 13 singers from a local cultural and heritage academy and 56 normal speakers from a local university, all were evaluated to have normal voices. Each participant underwent LHSV procedures to capture images of vocal fold vibration, which were subsequently analyzed to generate the LHSV-based measures. ResultsMale singers exhibited lower F0GAW, jitterGAW, shimmerGAW, and OQGAW than female singers. When compared to normal speakers, male singers demonstrated higher F0GAW, and lower jitterGAW and shimmerGAW. No difference in OQGAW was found between male singers and normal speakers. Female singers exhibited lower jitterGAW compared to normal speakers, but no differences were observed in shimmerGAW and OQGAW. The results of Nyquist plots indicated no gender-related associations between types of rim width and among singers. However, for rim pattern, male singers were associated with a higher percentage of clustered rim, suggesting more regular vocal fold vibration, compared to female singers and male normal speakers. ConclusionsSingers, particularly male singers, demonstrate distinct and potentially superior vocal fold vibrations compared to normal speakers, likely attributed to their regular vocal training, resulting in refined vocal fold configurations even during speaking. Despite the limited sample of singers, the study offers valuable insights into the vocal fold vibratory behaviors in singers analyzed using LHSV.

Empirical Distribution of Glottal Edges (EDGE): A Statistical Assessment of Vocal Fold Kinematics Using High-Speed Videoendoscopy.

Although laryngeal high-speed videoendoscopy (HSV) is crucial for studying vocal fold vibrations, its translation to clinical practice has been hindered by the large volume of data it produces and the difficulty in interpreting current analysis methods. Although image processing techniques have been developed to map spatial-temporal data into two-dimensional representations, they alter the geometrical construction of the glottis and do not provide standard quantitative features, thus challenging clinical interpretation. In response, we propose a new visualization and analysis framework for assessing the dynamics of vocal folds based on the empirical distribution of the glottal edge using HSV. This procedure analyzes vocal fold oscillations by preserving the shape of the glottis and quantifying the asymmetry between right and left vocal fold displacements along the anterior-posterior axis. This method was evaluated on four groups of participants: ten with normal voices, ten with vocal fold nodules, ten with muscle tension dysphonia, and two with unilateral vocal fold paralysis. The proposed method produces distinct representations for normal and pathological vocal fold vibratory behaviors and derived features based on amplitude and phase asymmetry metrics that show statistically significant differences between normal and pathological groups. Comparative analysis with state-of-the-art techniques indicates that our proposed method can complement the assessment of vocal fold vibration and enhance the clinical translation of HSV.

Vocal Vibration Opening Onset Position Abnormality in Patients With Adductor Spasmodic Dystonia by Using High-Speed Endoscopy

To analyze vocal fold vibration onset in patients with adductor laryngeal dystonia (ADLD) by analyzing vocal vibration opening onset position (VVOOP). Case-control study SETTING: A voice center. Eleven patients with ADLD diagnosed in our voice center were enrolled in the ADLD group. Eleven healthy subjects matched by exact age and gender to the ADLD patients were selected as the control group. All subjects underwent laryngeal high-speed video endoscopy. VVOOP and its change were assessed by two otolaryngologists. The multiline video kymography was used to analyze the openquotient (OQ) and standard deviation of OQ. VVOOP had more than one position in 54.6% (6/11) of the patients with ADLD, which was higher than the control group (P<0.05). VVOOP appeared in the front of the vocal fold in 54.6% (6/11) of patients with ADLD and in the back of the vocal fold in 81.8% (9/11) of patients with ADLD. VVOOP can be abnormal in 90.9% (10/11) of patients with ADLD, and the rate of VVOOP abnormality was higher than that of the control group (P<0.05). Of 11, 6 (54.6%) patients with ADLD had a variable VVOOP; thevariability rate of VVOOP was higher than that in the control group(P<0.05). OQ and OQ standard deviation in the ADLD group were significantly greater than in the control group (P<0.05). In patients with ADLD, vocal fold vibration was irregular, and VVOOP was abnormal and had a variable position and could reflect variability of the vocal vibration. Level 4.

Optical Flow Glottovibrogram for the examination of vocal fold pathology.

Laryngeal high-speed video endoscopy is performed to examine the cycles of vocal fold vibrations in detail and to diagnose voice abnormalities. One of the recent image processing techniques for visualizing vocal fold vibration is optical flow-based playbacks, which include optical flow kymograms (OFKG) for local dynamics, optical flow glottovibrogram (OFGVG) and glottal optical flow waveforms (GOFW) for global dynamics. In recent times, various optical flow computing algorithms have been developed. In this paper, we used four well-known optical flow algorithms Horn Schunk, Lucas Kanade, Gunnar Farneback, and TVL1 to construct the optical flow playbacks. The proposed playback reliability is examined by comparing them to traditional representations such as Phonovibrogram (PVG). Since PVG and OFGVG are interconnected, a comparison study was carried out to better comprehend their interaction.Clinical Relevance- Both OFGVG and PVG add to the precision of interpreting pathological conditions by offering complementary information to the conventional spatiotemporal representations.

Combining Artificial Intelligence and Automatic Analysis to Study of the Closure Characteristics of Healthy Human Vocal Cords during Phonation Using Synchronous Electroglottography and Laryngeal High-Speed Videoendoscopy

To use an artificial intelligence neural network to automatically analyze images from high-speed videoendoscopy and synchronous electroglottographic signals in healthy vocal folds to obtain the accurate glottal opening and closing instants in the glottal cycle.

Studying the glottal vibration onset and offset using laryngeal high-speed videoendoscopy in connected speech

Laryngeal high-speed videoendoscopy (HSV) is a powerful tool to study the detailed vibratory characteristics of the vocal folds. The use of HSV in connected speech enables us to study the transitory behaviors of the vocal folds during different vocalizations. The onset and offset of voicing contain critical information about the health and function of the vocal folds. In this work, HSV is used to study how the voicing begins/ends at the laryngeal level at the onset/offset of phonation. HSV data were obtained during production of connected speech from participants with non-pathological voices and patients with different neurogenic voice disorders. The data collection was performed using a Photron FASTCAM mini AX200 high-speed camera coupled with a flexible nasolaryngoscope. The glottal attack time and glottal offset time were measured at the onset and offset of different vocalizations, respectively, from the HSV data. The findings show differences in the onset and offset measures between the two groups of participants. The vocal fold vibratory behaviors will be discussed qualitatively and quantitatively using these measures. The use of HSV in connected speech could help us develop accurate voice assessment procedures to assess the vocal folds function during connected speech.

Laryngeal Imaging Study of Glottal Attack/Offset Time in Adductor Spasmodic Dysphonia during Connected Speech.

Adductor spasmodic dysphonia (AdSD) disrupts laryngeal muscle control during speech and, therefore, affects the onset and offset of phonation. In this study, the goal is to use laryngeal high-speed videoendoscopy (HSV) to measure the glottal attack time (GAT) and glottal offset time (GOT) during connected speech for normophonic (vocally normal) and AdSD voices. A monochrome HSV system was used to record readings of six CAPE-V sentences and part of the "Rainbow Passage" from the participants. Three raters visually analyzed the HSV data using a playback software to measure the GAT and GOT. The results show that the GAT was greater in the AdSD group than in the normophonic group; however, the clinical significance of the amount of this difference needs to be studied further. More variability was observed in both GATs and GOTs of the disorder group. Additionally, the GAT and GOT time series were found to be nonstationary for the AdSD group while they were stationary for the normophonic voices. This study shows that the GAT and GOT measures can be potentially used as objective markers to characterize AdSD. The findings will potentially help in the development of standardized measures for voice evaluation and the accurate diagnosis of AdSD.

Reabilitação vocal em cantoras com queixa vocal: análise da videoquimografia digital

ABSTRACT Purpose: to evaluate the effects of the Comprehensive Vocal Rehabilitation Program associated with the application of transcutaneous electrical nerve stimulation through digital kymography in singers with vocal complaints. Methods: an experimental intrasubject comparative study in 24 singers, who underwent the rehabilitation program associated with transcutaneous electrical nerve stimulation. They were assessed with laryngeal high-speed videoendoscopy before and after vocal rehabilitation. The paired t-test and Wilcoxon test were used to compare the two assessments. The significance level was set at 5%. Results: significant differences were found in the maximum opening, dominant amplitude of the opening variation and dominant frequency of the opening variation of the right vocal fold in the posterior glottic region, and in maximum opening, mean opening, dominant amplitude of the opening variation of the left vocal fold and dominant frequency of the opening variation of both vocal folds in the anterior glottic region. Conclusion: the results showed that the Comprehensive Vocal Rehabilitation Program associated with transcutaneous electrical stimulation decreased the opening amplitude of the vocal fold, increased the vibration frequency, and improved glottal closure in the anterior and posterior glottic regions.

Long-term performance assessment of fully automatic biomedical glottis segmentation at the point of care.

Deep Learning has a large impact on medical image analysis and lately has been adopted for clinical use at the point of care. However, there is only a small number of reports of long-term studies that show the performance of deep neural networks (DNNs) in such an environment. In this study, we measured the long-term performance of a clinically optimized DNN for laryngeal glottis segmentation. We have collected the video footage for two years from an AI-powered laryngeal high-speed videoendoscopy imaging system and found that the footage image quality is stable across time. Next, we determined the DNN segmentation performance on lossy and lossless compressed data revealing that only 9% of recordings contain segmentation artifacts. We found that lossy and lossless compression is on par for glottis segmentation, however, lossless compression provides significantly superior image quality. Lastly, we employed continual learning strategies to continuously incorporate new data into the DNN to remove the aforementioned segmentation artifacts. With modest manual intervention, we were able to largely alleviate these segmentation artifacts by up to 81%. We believe that our suggested deep learning-enhanced laryngeal imaging platform consistently provides clinically sound results, and together with our proposed continual learning scheme will have a long-lasting impact on the future of laryngeal imaging.

Deep-Learning-Based Representation of Vocal Fold Dynamics in Adductor Spasmodic Dysphonia during Connected Speech in High-Speed Videoendoscopy

Adductor spasmodic dysphonia (AdSD) is a neurogenic dystonia, which causes spasms of the laryngeal muscles. This disorder mainly affects production of connected speech. To understand how AdSD affects vocal fold (VF) movements and hence, the speech signal, it is necessary to study VF kinematics during the running speech. This paper introduces an automated method for analysis of VF vibrations in AdSD using laryngeal high-speed videoendoscopy (HSV) in running speech. A monochrome HSV system was used to obtain video recordings from vocally normal individuals and AdSD patients during production of the six CAPE-V sentences and the "Rainbow Passage." A deep neural network was designed based on the UNet architecture. The network was developed for glottal area segmentation in HSV data providing a tool for quantitative analysis of VF vibrations in both norm and AdSD. The network was trained and validated using the manually labeled HSV frames. After training the network, the segmentation quality was quantitatively evaluated against visual analysis results of a test dataset including segregated HSV frames and a short sequence of VF vibrations in consecutive frames. The developed convolutional network was successfully trained and demonstrated an accurate segmentation on the testing dataset with a mean Intersection over Union (IoU) of 0.81 and a mean Boundary-F1 score of 0.93. Moreover, the visual assessment of the automated technique showed an accurate detection of the glottal edges/area in the HSV data even with challenging image quality and excessive laryngeal maneuvers of AdSD patients during the running speech. The introduced automated approach provides an accurate representation of the glottal edges/area during connected speech in HSV data for norm and AdSD patients. This method facilitates the development of HSV-based measures to quantify VF dynamics in AdSD. Using HSV to automatically analyze VF vibrations in AdSD can allow for understanding AdSD vocal mechanisms and characteristics.

A single latent channel is sufficient for biomedical glottis segmentation

Glottis segmentation is a crucial step to quantify endoscopic footage in laryngeal high-speed videoendoscopy. Recent advances in deep neural networks for glottis segmentation allow for a fully automatic workflow. However, exact knowledge of integral parts of these deep segmentation networks remains unknown, and understanding the inner workings is crucial for acceptance in clinical practice. Here, we show that a single latent channel as a bottleneck layer is sufficient for glottal area segmentation using systematic ablations. We further demonstrate that the latent space is an abstraction of the glottal area segmentation relying on three spatially defined pixel subtypes allowing for a transparent interpretation. We further provide evidence that the latent space is highly correlated with the glottal area waveform, can be encoded with four bits, and decoded using lean decoders while maintaining a high reconstruction accuracy. Our findings suggest that glottis segmentation is a task that can be highly optimized to gain very efficient and explainable deep neural networks, important for application in the clinic. In the future, we believe that online deep learning-assisted monitoring is a game-changer in laryngeal examinations.

Simulated Laryngeal High-Speed Videos for the Study of Normal and Dysphonic Vocal Fold Vibration.

Laryngeal high-speed videoendoscopy (LHSV) has been recognized as a highly valuable modality for the scientific investigations of vocal fold (VF) vibrations. In contrast to stroboscopic imaging, LHSV enables visualizing aperiodic VF vibrations. However, the technique is less well established in the clinical care of disordered voices, partly because the properties of aperiodic vibration patterns are not yet described comprehensively. To address this, a computer model for simulation of VF vibration patterns observed in a variety of different phonation types is proposed. A previously published kinematic model of mucosal wave phenomena is generalized to be capable of left-right asymmetry and to simulate endoscopic videos instead of only kymograms of VF vibrations at single sagittal positions. The most influential control parameters are the glottal halfwidths, the oscillation frequencies, the amplitudes, and the phase delays. The presented videos demonstrate zipper-like vibration, pressed voice, voice onset, constant and time-varying left-right and anterior-posterior phase differences, as well as left-right frequency differences of the VF vibration. Video frames, videokymograms, phonovibrograms, glottal area waveforms, and waveforms of VF contact area relating to electroglottograms are shown, as well as selected kinematic parameters. The presented videos demonstrate the ability to produce vibration patterns that are similar to those typically seen in endoscopic videos obtained from vocally healthy and dysphonic speakers. https://doi.org/10.23641/asha.20151833.

Analysis of Laryngeal High-Speed Videoendoscopy recordings – ROI detection

Accurate detection of the glottis in video sequences obtained during the vocal cords examination using Laryngeal High-Speed Videoendoscopy (LHSV) is a prerequisite for the calculation of parameters describing vocal cord kinematics. This work presents the knowledge and methods related to the determination of the region of interest (ROI), which is one of the important steps in the processing of LHSV video sequences with the aim of automatic glottis detection. ROI is defined as the area between the vocal folds and anterior and posterior commissures.A number of methods have been published on this topic, which are used mainly in experimental LHSV video processing systems. To determine the ROI, we decided to use a method based on frequency analysis of oscillations of the vocal cord anatomical structures, which, as we know, has not been used in this context yet. The oscillation is represented by the change of brightness of the corresponding pixels in the LHSV images. The ROI is then successfully detected even in the relatively heterogeneous structure of tissues and fluids and for videos of various qualities, including luminance reflections, where the movement of the vocal cords can be detected.These methods extend the currently used system using a thresholding method for ROI detection and improve the success rate from 69% to 89%. These methods were tested on the LHSV video corpus, which contains 412 video sequences with different recording quality, diagnoses, and age groups of patients, obtained from ENT clinical practice.

A Deep Learning Approach for Quantifying Vocal Fold Dynamics During Connected Speech Using Laryngeal High-Speed Videoendoscopy.

Voice disorders are best assessed by examining vocal fold dynamics in connected speech. This can be achieved using flexible laryngeal high-speed videoendoscopy (HSV), which enables us to study vocal fold mechanics with high temporal details. Analysis of vocal fold vibration using HSV requires accurate segmentation of the vocal fold edges. This article presents an automated deep-learning scheme to segment the glottal area in HSV from which the glottal edges are derived during connected speech. Using a custom-built HSV system, data were obtained from a vocally healthy participant reciting the "Rainbow Passage." A deep neural network was designed for glottal area segmentation in the HSV data. A recently introduced hybrid approach by the authors was utilized as an automated labeling tool to train the network on a set of HSV frames, where the glottis region was automatically annotated during vocal fold vibrations. The network was then tested against manually segmented frames using different metrics, intersection over union (IoU), and Boundary F1 (BF) score, and its performance was assessed on various phonatory events on the HSV sequence. The designed network was successfully trained using the hybrid approach, without the need for manual labeling, and tested on the manually labeled data. The performance metrics showed a mean IoU of 0.82 and a mean BF score of 0.96. In addition, the evaluation assessment of the network's performance demonstrated an accurate segmentation of the glottal edges/area even during complex nonstationary phonatory events and when vocal folds were not vibrating, thus overcoming the limitations of the previous hybrid approach that could only be applied to the vibrating vocal folds. The introduced automated scheme guarantees accurate glottis representation in challenging color HSV data with lower image quality and excessive laryngeal maneuvers during all instances of connected speech. This facilitates the future development of HSV-based measures to assess the running vibratory characteristics of the vocal folds in speakers with and without voice disorder. https://doi.org/10.23641/asha.19798864.

The Effects of Hard Voice Onset on Objective Voice Function in Patients With Laryngopharyngeal Reflux

Laryngopharyngeal reflux (LPR) causes laryngopharyngeal hypersensitivity and laryngeal muscle hyperfunction, which may result in hard voice onset in patients with LPR. The purpose of this study is to examine the incidence of hard voice onset in patients with LPR and the effects of hard voice onset on objective voice function in patients with LPR. Forty patients with confirmed LPR were enrolled in the LPR group, and 40 healthy subjects were enrolled in the non-LPR group. Subjects underwent laryngeal high-speed videoendoscopy, and the presence or absence of hard voice onset in each subject was determined by two experienced laryngologists based on whether glottal closure was complete or incomplete before vocal fold vibration. Based on the results, the subjects with LPR were divided into a hard voice onset group and a non-hard voice onset group. The voice onset time (VOT) was measured and compared between the hard and non-hard voice onset groups within the LPR group. Laryngeal aerodynamic assessment was also carried out on the LPR group, and subglottal pressure, phonation threshold pressure (PTP), glottal resistance, and mean flow rate were measured. The voice acoustic signals of subjects were additionally analyzed in the LPR group, and the fundamental frequency, jitter, shimmer, and noise-harmony ratio were determined. The kappa statistic, chi-square test and independent-samples t test in SPSS were used for statistical analysis. The two laryngologists had substantial inter-rater consistency on the evaluation of hard voice onset and non-hard voice onset, with a kappa statistic of 0.71. In the LPR group, 42.5% of patients had hard voice onset (17/40), significantly more than in the non-LPR group (8/40, 20%) (P < 0.05). The VOT in the LPR group was significantly longer than in the non-LPR group (P < 0.05). Within the LPR group, the VOT, PTP and shimmer were significantly greater in the hard voice onset group than in the non-hard voice onset group (all P < 0.05). The other laryngeal aerodynamic parameters and acoustic parameters were not significantly different between the hard voice onset group and the non-hard voice onset group (P > 0.05). Changes in vocal production may occur in LPR patients, causing them to be more susceptible to hard voice onset. The patients with hard voice onset require longer VOT and greater PTP to initiate phonation.

Detection of Vocal Fold Image Obstructions in High-Speed Videoendoscopy During Connected Speech in Adductor Spasmodic Dysphonia: A Convolutional Neural Networks Approach

Adductor spasmodic dysphonia (AdSD) is a neurogenic voice disorder, affecting the intrinsic laryngeal muscle control. AdSD leads to involuntary laryngeal spasms and only reveals during connected speech. Laryngeal high-speed videoendoscopy (HSV) coupled with a flexible fiberoptic endoscope provides a unique opportunity to study voice production and visualize the vocal fold vibrations in AdSD during speech. The goal of this study is to automatically detect instances during which the image of the vocal folds is optically obstructed in HSV recordings obtained during connected speech. HSV data were recorded from vocally normal adults and patients with AdSD during reading of the "Rainbow Passage", six CAPE-V sentences, and production of the vowel /i/. A convolutional neural network was developed and trained as a classifier to detect obstructed/unobstructed vocal folds in HSV frames. Manually labelled data were used for training, validating, and testing of the network. Moreover, a comprehensive robustness evaluation was conducted to compare the performance of the developed classifier and visual analysis of HSV data. The developed convolutional neural network was able to automatically detect the vocal fold obstructions in HSV data in vocally normal participants and AdSD patients. The trained network was tested successfully and showed an overall classification accuracy of 94.18% on the testing dataset. The robustness evaluation showed an average overall accuracy of 94.81% on a massive number of HSV frames demonstrating the high robustness of the introduced technique while keeping a high level of accuracy. The proposed approach can be used for efficient analysis of HSV data to study laryngeal maneuvers in patients with AdSD during connected speech. Additionally, this method will facilitate development of vocal fold vibratory measures for HSV frames with an unobstructed view of the vocal folds. Indicating parts of connected speech that provide an unobstructed view of the vocal folds can be used for developing optimal passages for precise HSV examination during connected speech and subject-specific clinical voice assessment protocols.

Segmentation of Glottal Images from High-Speed Videoendoscopy Optimized by Synchronous Acoustic Recordings.

Laryngeal high-speed videoendoscopy (LHSV) is an imaging technique offering novel visualization quality of the vibratory activity of the vocal folds. However, in most image analysis methods, the interaction of the medical personnel and access to ground truth annotations are required to achieve accurate detection of vocal folds edges. In our fully automatic method, we combine video and acoustic data that are synchronously recorded during the laryngeal endoscopy. We show that the image segmentation algorithm of the glottal area can be optimized by matching the Fourier spectra of the pre-processed video and the spectra of the acoustic recording during the phonation of sustained vowel /i:/. We verify our method on a set of LHSV recordings taken from subjects with normophonic voice and patients with voice disorders due to glottal insufficiency. We show that the computed geometric indices of the glottal area make it possible to discriminate between normal and pathologic voices. The median of the Open Quotient and Minimal Relative Glottal Area values for healthy subjects were 0.69 and 0.06, respectively, while for dysphonic subjects were 1 and 0.35, respectively. We also validate these results using independent phoniatrician experts.

Functional Analysis of Injectable Substance Treatment on Surgically Injured Rabbit Vocal Folds

The objective of this study was to evaluate the efficacy of immediate injection treatments of dexamethasone, hyaluronic acid (HA)/gelatin (Ge) hydrogel and glycol-chitosan solution on the phonatory function of rabbit larynges at 42 days after surgical injury of the vocal folds, piloting a novel ex vivo phonatory functional analysis protocol. A modified microflap procedure was performed on the left vocal fold of 12 rabbits to induce an acute injury. Animals were randomized into one of four treatment groups with 0.1 mL injections of dexamethasone, HA/Ge hydrogel, glycol-chitosan or saline as control. The left mid vocal fold lamina propria was injected immediately following injury. The right vocal fold served as an uninjured control. Larynges were harvested at Day 42 after injection, then were subjected to airflow-bench evaluation. Acoustic, aerodynamic and laryngeal high-speed videoendoscopy (HSV) analyses were performed. HSV segments of the vibrating vocal folds were rated by three expert laryngologists. Six parameters related to vocal fold vibratory characteristics were evaluated on a Likert scale. The fundamental frequency, one possible surrogate of vocal fold stiffness and scarring, was lower in the dexamethasone and HA/Ge hydrogel treatment groups compared to that of the saline control (411.52±11.63 Hz). The lowest fundamental frequency value was observed in the dexamethasone group (348.79±14.99 Hz). Expert visual ratings of the HSV segments indicated an overall positive outcome in the dexamethasone treatment group, though the impacts were below statistical significance. Dexamethasone injections might be used as an adjunctive option for iatrogenic vocal fold scarring. An increased sample size, histological correlate, and experimental method improvements will be needed to confirm this finding. Results suggested a promising use of HSV and acoustic analysis techniques to identify and monitor post-surgical vocal fold repair and scarring, providing a useful tool for future studies of vocal fold scar treatments.

Direct measurement and modeling of intraglottal, subglottal, and vocal fold collision pressures during phonation in an individual with a hemilaryngectomy.

The purpose of this paper is to report on the first in vivo application of a recently developed transoral, dual-sensor pressure probe that directly measures intraglottal, subglottal, and vocal fold collision pressures during phonation. Synchronous measurement of intraglottal and subglottal pressures was accomplished using two miniature pressure sensors mounted on the end of the probe and inserted transorally in a 78-year-old male who had previously undergone surgical removal of his right vocal fold for treatment of laryngeal cancer. The endoscopist used one hand to position the custom probe against the surgically medialized scar band that replaced the right vocal fold and used the other hand to position a transoral endoscope to record laryngeal high-speed videoendoscopy of the vibrating left vocal fold contacting the pressure probe. Visualization of the larynx during sustained phonation allowed the endoscopist to place the dual-sensor pressure probe such that the proximal sensor was positioned intraglottally and the distal sensor subglottally. The proximal pressure sensor was verified to be in the strike zone of vocal fold collision during phonation when the intraglottal pressure signal exhibited three characteristics: an impulsive peak at the start of the closed phase, rounded peak during the open phase, and minimum value around zero immediately preceding the impulsive peak of the subsequent phonatory cycle. Numerical voice production modeling was applied to validate model-based predictions of vocal fold collision pressure using kinematic vocal fold measures. The results successfully demonstrated feasibility of in vivo measurement of vocal fold collision pressure in an individual with a hemilaryngectomy, motivating ongoing data collection that is designed to aid in the development of vocal dose measures that incorporate vocal fold impact collision and stresses.