Peer-Reviewed Publication
Laryngoscope2026;136(6):2511-2518.June 1, 2026Journal Article
Exploring Real-Time Tracking of Vocal Fold Polyps in Video-Stroboscopy Using Deep Learning.
Sanjana Kaza1, Abhinita S Mohanty2, Aisha Serpedin3, Jenny Yau3, Yeo Eun Kim3, Rachel B Kutler3, Olivier Elemento4, Lucian Sulica3, Pegah Khosravi5,6, Anaïs Rameau3
1Cornell Tech, New York, New York, USA.
2Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA.
3Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medicine, New York, New York, USA.
4Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA.
5Department of Clinical Sciences, College of Medicine, University of Central Florida, Orlando, Florida, USA.
6Department of Computer Science, Institute for Artificial Intelligence, University of Central Florida, Orlando, Florida, USA.
Abstract
OBJECTIVE: To develop and evaluate a deep learning object detection system for identifying vocal fold polyps in stroboscopic video frames using You Only Look Once (YOLO), and to assess the added benefit of temporal tracking on detection performance.
METHODS: A retrospective dataset of 12,742 annotated frames from 55 laryngoscopy video recordings was annotated with bounding boxes identifying vocal…
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