<i>Objective:</i> This study focuses on the relation between objective voice quality and the self-perception of a voice handicap. <i>Patients and Methods:</i> The study group consisted of 86 German-speaking patients (51 women, 35 men) suffering from benign dysphonia. The test persons completed the German version of the Voice-Related Quality of Life (V-RQOL) Questionnaire without prior information about their diagnosis and underwent voice analysis with the Dysphonia Severity Index (DSI) being the parameter of this study. <i>Results:</i> No correlation between V-RQOL and DSI could be found (p > 0.05). On the V-RQOL, women scored worse than men, but not at a significant level. Patients with dysphonia of organic origin scored significantly worse than patients with functional dysphonia (p = 0.026). On the DSI, men’s values were significantly lower than women’s (p = 0.001). Organic dysphonia caused significantly lower DSI values than functional dysphonia (p = 0.011). <i>Conclusion:</i> Objective voice quality and the individual perception of voice quality by the patient are independent parameters. Both need to be assessed in clinical practice.
Glottis segmentation is a crucial step to quantify endoscopic footage in laryngeal high-speed videoendoscopy. Recent advances in using deep neural networks for glottis segmentation allow a fully automatic workflow. However, exact knowledge of integral parts of these segmentation deep neural networks remains unknown. Here, we show using systematic ablations that a single latent channel as bottleneck layer is sufficient for glottal area segmentation. We further show that the latent space is an abstraction of the glottal area segmentation relying on three spatially defined pixel subtypes. We 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 clinical applicable deep neural networks. In future, we believe that online deep learning-assisted monitoring is a game changer in laryngeal examinations.
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