Clinical Applications of Deep Learning for Glottal Area Segmentation and Glottal Area Waveform Feature Computation.

Goal: Analysis of the glottal area during vocal fold vibration has gained increasing attention. However, traditional analysis requires manual, frame-by-frame glottal area annotation to compute the glottal area waveform, a time-consuming, and error-prone process. Methods: This study proposes an automated system for glottal area segmentation and glottal area waveform feature extraction from 36 videostroboscopy recordings of 23 patients with vocal fold nodules. The system integrates YOLO and U-Net architecture for glottis detection and segmentation. Subject-independent 5-fold cross-validation was performed on 5017 annotated frames. Results: The system achieved an average Intersection over Union of 92.8%, and a Dice Similarity Coefficient of 95.8%, substantially outperforming thresholding and edge-based baselines. Computation time was reduced by 27.7 -folds compared with manual method. Applied to 14 patients undergoing voice treatment, the system detected consistent trends in glottal area dynamics post-treatment. Conclusion: The system enhances efficiency and accuracy of glottal area waveform analysis and demonstrates clinical utility for laryngeal assessment.