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arXiv · 2512.23177

Machine Learning-Assisted Vocal Cord Ultrasound Examination: Project VIPR

Will Sebelik-Lassiter·Evan Schubert·Muhammad Alliyu·Quentin Robbins·Excel Olatunji·Mustafa Barry
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Abstract

Intro: Vocal cord ultrasound (VCUS) has emerged as a less invasive and better tolerated examination technique, but its accuracy is operator dependent. This research aims to apply a machine learning-assisted algorithm to automatically identify the vocal cords and distinguish normal vocal cord images from vocal cord paralysis (VCP). Methods: VCUS videos were acquired from 30 volunteers, which were split into still frames and cropped to a uniform size. Healthy and simulated VCP images were used as training data for vocal cord segmentation and VCP classification models. Results: The vocal cord segmentation model achieved a validation accuracy of 96%, while the best classification model (VIPRnet) achieved a validation accuracy of 99%. Conclusion: Machine learning-assisted analysis of VCUS shows great promise in improving diagnostic accuracy over operator-dependent human interpretation.

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Will Sebelik-Lassiter, Evan Schubert, Muhammad Alliyu, Quentin Robbins, Excel Olatunji, Mustafa Barry. 2025-12-29. Machine Learning-Assisted Vocal Cord Ultrasound Examination: Project VIPR. https://arxiv.org/abs/2512.23177

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