{"ID":2824193,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.23177","arxiv_id":"2512.23177","title":"Machine Learning-Assisted Vocal Cord Ultrasound Examination: Project VIPR","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.","short_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 para...","url_abs":"https://arxiv.org/abs/2512.23177","url_pdf":"https://arxiv.org/pdf/2512.23177v1","authors":"[\"Will Sebelik-Lassiter\",\"Evan Schubert\",\"Muhammad Alliyu\",\"Quentin Robbins\",\"Excel Olatunji\",\"Mustafa Barry\"]","published":"2025-12-29T03:35:11Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CE\",\"cs.CV\"]","methods":"[]","has_code":false}