Assessment of voice quality: Current state-of-the-art

Purpose High-speed videoendoscopy (HSV) is an emerging, but barely used, endoscopy technique in the clinic to assess and diagnose voice disorders because of the lack of dedicated software to analyze the data. HSV allows to quantify the vocal fold oscillations by segmenting the glottal area. This challenging task has been tackled by various studies; however, the proposed approaches are mostly limited and not suitable for daily clinical routine. Method We developed a user-friendly software in C# that allows the editing, motion correction, segmentation, and quantitative analysis of HSV data. We further provide pretrained deep neural networks for fully automatic glottis segmentation. Results We freely provide our software Glottis Analysis Tools (GAT). Using GAT, we provide a general threshold-based region growing platform that enables the user to analyze data from various sources, such as in vivo recordings, ex vivo recordings, and high-speed footage of artificial vocal folds. Additionally, especially for in vivo recordings, we provide three robust neural networks at various speed and quality settings to allow a fully automatic glottis segmentation needed for application by untrained personnel. GAT further evaluates video and audio data in parallel and is able to extract various features from the video data, among others the glottal area waveform, that is, the changing glottal area over time. In total, GAT provides 79 unique quantitative analysis parameters for video- and audio-based signals. Many of these parameters have already been shown to reflect voice disorders, highlighting the clinical importance and usefulness of the GAT software. Conclusion GAT is a unique tool to process HSV and audio data to determine quantitative, clinically relevant parameters for research, diagnosis, and treatment of laryngeal disorders. Supplemental Material https://doi.org/10.23641/asha.14575533.

https://pubs.asha.org/doi/pdf/10.1044/2021_JSLHR-20-00498

A Deep Learning Enhanced Novel Software Tool for Laryngeal Dynamics Analysis.

Laryngeal endoscopy is one of the primary diagnostic tools for laryngeal disorders. The main techniques are videostroboscopy and lately high-speed video endoscopy. Unfortunately, due to the restricting anatomy of the larynx and technical limitations of the recording equipment, many videos suffer from insufficient illumination, which complicates clinical examination and analysis. This work presents an approach to enhance low-light images from high-speed video endoscopy using a convolutional neural network. We introduce a new technique to generate realistically darkened training samples using Perlin noise. Extensive data augmentation is employed to cope with the limited training data allowing training with just 55 videos. The approach is compared against four state-of-the-art low-light enhancement methods and statistically significantly outperforms each on a no-reference (NIQE) and two full-reference (PSNR, SSIM) image quality metrics. The presented approach can be run on consumer-grade hardware and is thereby directly applicable in a clinical context. It is likely transferable to similar techniques such as videostroboscopy.

/pdf/low-light-image-enhancement-of-high-speed-endoscopic-videos-1gdrcw0i58.pdf

Low-light image enhancement of high-speed endoscopic videos using a convolutional neural network.

Realistic glottal motion and airflow rate during human breathing

Objectives/Hypothesis

Quantitative analysis of endoscopic high-speed video recordings of vocal fold vibrations has been growing in importance in recent years. The videos have mainly been analyzed using subjective evaluation, but this is examiner dependent, and the results show inadequate interobserver agreement. The aims of this study were therefore to identify appropriate objective parameters for analyzing high-speed recordings to differentiate healthy voice production from organic disorders.



Study Design

 



Methods

A total of 152 females were examined, divided into 77 healthy and 75 with four different pathological conditions: laryngeal epithelial thickening, Reinke edema, vocal fold polyps, and vocal fold cysts. Vocal fold vibrations were recorded with a high-speed camera (4,000 Hz, 256 × 256 pixels) during sustained phonation. Parameters computed from the glottal area waveform (GAW) and from phonovibrogram (PVG) were analyzed. Multiparametric linear discriminant analysis was performed to classify pathological conditions versus the healthy group.



Results

Twenty of 44 parameters were identified that are capable of distinguishing between the individual types of pathology. PVG parameters showed better performance than GAW parameters. Parameters representing vibrational periodicity via standard deviation showed better performance than absolute parameters. In addition, linear discriminant analysis achieved reliable differentiation between healthy and pathological vocal fold vibrations: 72% for the five-class problem (all groups separately) and 88% for the two-class problem (healthy vs. all pathologies taken as one class).



Conclusions

The study succeeded in defining objective parameters for analyzing endoscopic high-speed videos and suggesting first parameters for differentiation between healthy dynamics and dynamics of organic pathologies.



Level of Evidence

3b

Quantitative analysis of organic vocal fold pathologies in females by high‐speed endoscopy

Clinical Analysis Methods of Voice Disorders

/pdf/the-anti-iglon5-syndrome-in-clinical-neurology-report-of-two-1x8wblpj.pdf

[The anti-IgLON5 syndrome in clinical neurology-Report of two cases].

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Clinical Analysis Methods of Voice Disorders

[The anti-IgLON5 syndrome in clinical neurology-Report of two cases].