Multimodal Analysis of Structural Voice Disorders Based on Speech and Stroboscopic Laryngoscope Video
NCT ID: NCT05348031
Last Updated: 2022-04-27
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
1 participants
OBSERVATIONAL
2022-05-06
2027-02-20
Brief Summary
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Detailed Description
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2. Detection and Classification of Acoustic Lesions Based on Deep Learning of Images
3. Detection and Classification of Acoustic Lesions Based on Deep Learning Based on Multimodality
Conditions
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Study Design
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CASE_ONLY
RETROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
2. Patients with voice disorders caused by various causes except laryngeal cancer, laryngeal precancerous lesions, and benign laryngeal lesions
3. The audio quality is not clear, the stroboscopic laryngoscope does not clearly display the anatomical area related to the glottis, and it is underexposed and blocked;
20 Years
80 Years
ALL
Yes
Sponsors
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Duke Kunshan University
OTHER
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
OTHER
Responsible Party
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Principal Investigators
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YueXin Cai
Role: STUDY_CHAIR
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Central Contacts
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References
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Martínez, David, Lleida Eduardo, Ortega Alfonso,Miguel Antonio, Villalba Jesús. Voice pathology detection on the Saarbrücken voice database with calibration and fusion of scores using multifocal toolkit. Advances in Speech and Language Technologies for Iberian Languages. Springer, Berlin, Heidelberg, 2012. 99-109
Hegde S, Shetty S, Rai S, Dodderi T. A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders. J Voice. 2019 Nov;33(6):947.e11-947.e33. doi: 10.1016/j.jvoice.2018.07.014. Epub 2018 Oct 11.
Al-Nasheri A, Muhammad G, Alsulaiman M, Ali Z. Investigation of Voice Pathology Detection and Classification on Different Frequency Regions Using Correlation Functions. J Voice. 2017 Jan;31(1):3-15. doi: 10.1016/j.jvoice.2016.01.014. Epub 2016 Mar 15.
.Chuang, ZY,YuXT,Chen JY, Hsu YT,Xu ZZ,Wang CT,Lin FC,Fang SH. DNN-based approach to detect and classify pathological voice. In Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 10-13 December 2018
Fang SH, Tsao Y, Hsiao MJ, Chen JY, Lai YH, Lin FC, Wang CT. Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach. J Voice. 2019 Sep;33(5):634-641. doi: 10.1016/j.jvoice.2018.02.003. Epub 2018 Mar 19.
Bethani Gty As H , Suwandi, Anggraini C D . Classification System Vocal Cords Disease Using Digital Image Processing.The 2019 IEEE International Conference on industry 4.0,Artifical Intelligence,and Communications Technology.2019.129-132
Unger J, Lohscheller J, Reiter M, Eder K, Betz CS, Schuster M. A noninvasive procedure for early-stage discrimination of malignant and precancerous vocal fold lesions based on laryngeal dynamics analysis. Cancer Res. 2015 Jan 1;75(1):31-9. doi: 10.1158/0008-5472.CAN-14-1458. Epub 2014 Nov 4.
Xiong H, Lin P, Yu JG, Ye J, Xiao L, Tao Y, Jiang Z, Lin W, Liu M, Xu J, Hu W, Lu Y, Liu H, Li Y, Zheng Y, Yang H. Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images. EBioMedicine. 2019 Oct;48:92-99. doi: 10.1016/j.ebiom.2019.08.075. Epub 2019 Oct 5.
Kim H, Jeon J, Han YJ, Joo Y, Lee J, Lee S, Im S. Convolutional Neural Network Classifies Pathological Voice Change in Laryngeal Cancer with High Accuracy. J Clin Med. 2020 Oct 25;9(11):3415. doi: 10.3390/jcm9113415.
Godino-Llorente JI, Gomez-Vilda P. Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors. IEEE Trans Biomed Eng. 2004 Feb;51(2):380-4. doi: 10.1109/TBME.2003.820386.
Ren J, Jing X, Wang J, Ren X, Xu Y, Yang Q, Ma L, Sun Y, Xu W, Yang N, Zou J, Zheng Y, Chen M, Gan W, Xiang T, An J, Liu R, Lv C, Lin K, Zheng X, Lou F, Rao Y, Yang H, Liu K, Liu G, Lu T, Zheng X, Zhao Y. Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique. Laryngoscope. 2020 Nov;130(11):E686-E693. doi: 10.1002/lary.28539. Epub 2020 Feb 18.
Bainbridge KE, Roy N, Losonczy KG, Hoffman HJ, Cohen SM. Voice disorders and associated risk markers among young adults in the United States. Laryngoscope. 2017 Sep;127(9):2093-2099. doi: 10.1002/lary.26465. Epub 2016 Dec 23.
Other Identifiers
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SYSEC-KY-KS-2022-040
Identifier Type: -
Identifier Source: org_study_id
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