Background: Colorectal cancer (CRC) claims 900,000 lives per year. Colonoscopy offers reliable detection, but with low patient adherence rates. To significantly reduce CRC incidence and mortality, a more convenient screening measure for advanced precancerous lesions (APL) and CRC is urgently needed.
View Article and Find Full Text PDFObjectives: This study investigates the use of sustained phonations recorded during high-speed videoendoscopy (HSV) for machine learning-based assessment of hoarseness severity (H). The performance of this approach is compared with conventional recordings obtained during voice therapy to evaluate key differences and limitations of HSV-derived acoustic recordings.
Methods: A database of 617 voice recordings with a duration of 250 ms was gathered during HSV examination (HS).
Quantification of voice physiology has been a key research goal. Segmenting the glottal area to describe the vocal fold motion has seen increased attention in the last two decades. However, researchers struggled to fully automatize the segmentation task.
View Article and Find Full Text PDFHuman vocalization is a complex process that is still only partially understood. Previous studies have suggested the possibility of a localized neuromuscular network of the larynx. Here we investigate this structure in human dissection specimens using multiple immunofluorescence and transmission electron microscopy (TEM).
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