ArticleThe Laryngoscope2026
Feasibility of Real-Time Automated Vocal Fold Motion Tracking for In-Office Laryngoscopy.
Article in The Laryngoscope, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Videomics and artificial intelligence in endoscopic diagnosis of laryngeal lesions: mapping current evidence through a scoping review.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2026Review
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Authors and funding
5 authors.
Funding
Abstract
objectivesMajor advancements have been made in applying artificial intelligence and computer vision to analyze videolaryngoscopy data. These models are limited to post hoc analysis and are aimed at research settings. In this work, we assess the feasibility of a real-time solution for automated vocal fold tracking during in-office laryngoscopy.
methodsWe trained a keypoint detection model that tracks 39 individual keypoints of the larynx, optimized for real-time deployment, on 1254 frames extracted from 57 laryngoscopy videos. Images were annotated with keypoints along the true and false vocal folds, and supraglottic structures. After training, the model was evaluated on a validation dataset of 140 test images, and second independent test dataset of 50 images. Performance was assessed by comparing manual keypoint annotations to predicted annotations and computing mean keypoint accuracy (
resultsThe model demonstrated robust detection accuracy with an
conclusionThe model accurately tracks laryngeal structures with strong detection, localization, and consistency, is compatible with affordable hardware, and enables real-time metric development. LEVEL OF EVIDENCE: 4:
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