Machine Learning-Assisted Vocal Cord Ultrasound Examination: Project VIPR
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908737047887872 |
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| author | Sebelik-Lassiter, Will Schubert, Evan Alliyu, Muhammad Robbins, Quentin Olatunji, Excel Barry, Mustafa |
| author_facet | Sebelik-Lassiter, Will Schubert, Evan Alliyu, Muhammad Robbins, Quentin Olatunji, Excel Barry, Mustafa |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_23177 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Assisted Vocal Cord Ultrasound Examination: Project VIPR Sebelik-Lassiter, Will Schubert, Evan Alliyu, Muhammad Robbins, Quentin Olatunji, Excel Barry, Mustafa Machine Learning Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition 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. |
| title | Machine Learning-Assisted Vocal Cord Ultrasound Examination: Project VIPR |
| topic | Machine Learning Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.23177 |