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 fol...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-09797-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849332869280825344 |
|---|---|
| author | Kyoung Ok Yang So Young Kim Chang Won Kang Jeong Seon Choi Yong Bae Ji Kyung Tae Jun Won Choi Chang Myeon Song |
| author_facet | Kyoung Ok Yang So Young Kim Chang Won Kang Jeong Seon Choi Yong Bae Ji Kyung Tae Jun Won Choi Chang Myeon Song |
| author_sort | Kyoung Ok Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1b3a3eb2e726421d98d163c6aabd9682 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1b3a3eb2e726421d98d163c6aabd96822025-08-20T03:46:04ZengNature PortfolioScientific Reports2045-23222025-07-0115111010.1038/s41598-025-09797-zDiagnosis of unilateral vocal fold paralysis using auto-diagnostic deep learning modelKyoung Ok Yang0So Young Kim1Chang Won Kang2Jeong Seon Choi3Yong Bae Ji4Kyung Tae5Jun Won Choi6Chang Myeon Song7Department of Artificial Intelligence, Hanyang UniversityDepartment of Anatomy and Cell Biology, College of Medicine, Seoul National UniversityDepartment of Artificial Intelligence, Hanyang UniversityDepartment of Artificial Intelligence, Hanyang UniversityDepartment of Otolaryngology-Head and Neck Surgery, Hanyang University College of MedicineDepartment of Otolaryngology-Head and Neck Surgery, Hanyang University College of MedicineDepartment of Electrical and Computer Engineering, Seoul National UniversityDepartment of Otolaryngology-Head and Neck Surgery, Hanyang University College of MedicineAbstract 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.https://doi.org/10.1038/s41598-025-09797-zVocal fold paralysisDeep learning modelAuto-diagnosisLaryngeal videoendoscopyMulti-task learningVocal fold movement |
| spellingShingle | Kyoung Ok Yang So Young Kim Chang Won Kang Jeong Seon Choi Yong Bae Ji Kyung Tae Jun Won Choi Chang Myeon Song Diagnosis of unilateral vocal fold paralysis using auto-diagnostic deep learning model Scientific Reports Vocal fold paralysis Deep learning model Auto-diagnosis Laryngeal videoendoscopy Multi-task learning Vocal fold movement |
| title | Diagnosis of unilateral vocal fold paralysis using auto-diagnostic deep learning model |
| title_full | Diagnosis of unilateral vocal fold paralysis using auto-diagnostic deep learning model |
| title_fullStr | Diagnosis of unilateral vocal fold paralysis using auto-diagnostic deep learning model |
| title_full_unstemmed | Diagnosis of unilateral vocal fold paralysis using auto-diagnostic deep learning model |
| title_short | Diagnosis of unilateral vocal fold paralysis using auto-diagnostic deep learning model |
| title_sort | diagnosis of unilateral vocal fold paralysis using auto diagnostic deep learning model |
| topic | Vocal fold paralysis Deep learning model Auto-diagnosis Laryngeal videoendoscopy Multi-task learning Vocal fold movement |
| url | https://doi.org/10.1038/s41598-025-09797-z |
| work_keys_str_mv | AT kyoungokyang diagnosisofunilateralvocalfoldparalysisusingautodiagnosticdeeplearningmodel AT soyoungkim diagnosisofunilateralvocalfoldparalysisusingautodiagnosticdeeplearningmodel AT changwonkang diagnosisofunilateralvocalfoldparalysisusingautodiagnosticdeeplearningmodel AT jeongseonchoi diagnosisofunilateralvocalfoldparalysisusingautodiagnosticdeeplearningmodel AT yongbaeji diagnosisofunilateralvocalfoldparalysisusingautodiagnosticdeeplearningmodel AT kyungtae diagnosisofunilateralvocalfoldparalysisusingautodiagnosticdeeplearningmodel AT junwonchoi diagnosisofunilateralvocalfoldparalysisusingautodiagnosticdeeplearningmodel AT changmyeonsong diagnosisofunilateralvocalfoldparalysisusingautodiagnosticdeeplearningmodel |