Diagnosis of unilateral vocal fold paralysis using auto-diagnostic deep learning model

Abstract

Unilateral vocal fold paralysis (UVFP) is a condition characterized by impaired vocal fold mobility, typically diagnosed using laryngeal videoendoscopy. While deep learning (DL) models using static images have been explored for UVFP detection, they often lack the ability to assess vocal fold dynamics. We developed an auto-diagnostic DL system for UVFP using both image-based and video-based models. Using laryngeal videoendoscopic data from 500 participants, the model was trained and validated on 2639 video clips. The image-based DL model achieved over 98% accuracy for UVFP detection, but demonstrated limited performance in predicting laterality and paralysis type. In contrast, the video-based model achieved comparable accuracy (about 99%) in detecting UVFP, and substantially higher accuracy in predicting laterality and paralysis type, outperforming the image-based model in overall diagnostic utility. These results demonstrate the advantages of incorporating temporal motion cues in video-based analysis and support the use of DL for comprehensive, multi-task assessment of UVFP. This automated approach demonstrates high diagnostic performance and may serve as a complementary tool to assist clinicians in the assessment of UVFP, particularly in enhancing workflow efficiency and supporting multi-dimensional interpretation of laryngeal motion.