Multivariate Analysis of Vocal Fold Vibrations in Normal Speakers Using High-Speed Digital Imaging

Gender- and Age- Stratified Normative Voice Data in Japanese-Speaking Subjects: Analysis of Sustained Habitual Phonations

INTRODUCTION

There is no normative voice dataset for Japanese speakers in the English literature. We constructed age- and gender-stratified normative voice data with the assistance of vocally healthy Japanese speakers.

METHODS

A total of 111 vocally healthy Japanese speakers (42 men, 69 women) were divided into young (13 men, 30 women), middle-aged (18 men, 27 women), and elderly (11 men, 12 women) groups. Participants underwent aerodynamic, acoustic, and audio-perceptual studies of sustained habitual vowel phonations, and the obtained data were statistically analyzed in terms of age and gender.

RESULTS

Both gender- and age-related differences were noted in fundamental frequencies, sound pressure level, shimmer, and amplitude perturbation quotient, while only gender-related differences were noted in mean flow rate and only age-related changes were observed in subglottal pressure; laryngeal resistance; and G, R, B, and S scores of the GRBAS scale. The gender- and age-related difference data were comparable with the reported data in other languages, ethnicities, or countries.

CONCLUSIONS

The present study is the first to provide a database of normative voice data of Japanese speakers. The idiosyncrasy of Japanese is considered minor in sustained habitual vowel phonations.

High-Speed Videoendoscopy Enhances the Objective Assessment of Glottic Organic Lesions: A Case-Control Study with Multivariable Data-Mining Model Development

The aim of the study was to utilize a quantitative assessment of the vibratory characteristics of vocal folds in diagnosing benign and malignant lesions of the glottis using high-speed videolaryngoscopy (HSV).

METHODS

Case-control study including 100 patients with unilateral vocal fold lesions in comparison to 38 normophonic subjects. Quantitative assessment with the determination of vocal fold oscillation parameters was performed based on HSV kymography. Machine-learning predictive models were developed and validated.

RESULTS

All calculated parameters differed significantly between healthy subjects and patients with organic lesions. The first predictive model distinguishing any organic lesion patients from healthy subjects reached an area under the curve (AUC) equal to 0.983 and presented with 89.3% accuracy, 97.0% sensitivity, and 71.4% specificity on the testing set. The second model identifying malignancy among organic lesions reached an AUC equal to 0.85 and presented with 80.6% accuracy, 100% sensitivity, and 71.1% specificity on the training set. Important predictive factors for the models were frequency perturbation measures.

CONCLUSIONS

The standard protocol for distinguishing between benign and malignant lesions continues to be clinical evaluation by an experienced ENT specialist and confirmed by histopathological examination. Our findings did suggest that advanced machine learning models, which consider the complex interactions present in HSV data, could potentially indicate a heightened risk of malignancy. Therefore, this technology could prove pivotal in aiding in early cancer detection, thereby emphasizing the need for further investigation and validation.