Abstract
OBJECTIVES: Major 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. METHODS: We 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 ( mKA ), and by calculating temporal consistency ( mTC ) from a calibration video clip. The live feed of a flexible laryngoscope, mounted to an endoscopic tower, was passed via a video capture card into a laptop running the motion tracking model in real-time. RESULTS: The model demonstrated robust detection accuracy with an mKA of 85% and 75% with validation and test datasets, and promising temporal consistence mTC of 5.85 average pixel movement. During testing, the model ran at 30 frames per second, with minimal appreciable latency while processing the laryngoscope's live feed, ensuring the laryngologist's work was not affected. CONCLUSION: The model accurately tracks laryngeal structures with strong detection, localization, and consistency, is compatible with affordable hardware, and enables real-time metric development.