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.
@article{arxiv.2512.23177,
title = {Machine Learning-Assisted Vocal Cord Ultrasound Examination: Project VIPR},
author = {Will Sebelik-Lassiter and Evan Schubert and Muhammad Alliyu and Quentin Robbins and Excel Olatunji and Mustafa Barry},
journal= {arXiv preprint arXiv:2512.23177},
year = {2025}
}
Comments
Won Best Undergraduate Research Paper at the 2025 Midwest Instruction & Computing Symposium (MICS)