Multimodal Analysis of Structural Voice Disorders Based on Speech and Stroboscopic Laryngoscope Video

NCT05348031 | Not Yet Recruiting

• 1 other identifier: SYSEC-KY-KS-2022-040
• Study Type: observational
• Target: 1
• Locations: 0 countries
• Sites: N/A

Brief Summary

This study intends to collect clinical data such as strobary laryngoscope images and vowel audio data of patients with structural voice disorders and healthy individuals, and to establish a multimodal voice disorder diagnosis system model by using deep learning algorithms. Multi-classification of diseases that cause voice disorders can be applied to patients with voice disorders but undiagnosed in clinical practice, thereby assisting clinicians in diagnosing diseases and reducing misdiagnosis and missed diagnosis. In addition, some patients with voice disorders can be managed remotely through the audio diagnosis model, and better follow-up and treatment suggestions can be given to them. Remote voice therapy can alleviate the current situation of the shortage of speech therapists in remote areas of our country, and increase the number of patients who need voice therapy. opportunity. Remote voice therapy is more cost-effective, more flexible in time, and more cost-effective.

Conditions

Voice Disorders

Keywords

deep learning; multimodality; structural voice disorders; stroboscopic laryngoscope video; Speech

Outcome Measures

Primary Outcomes (3)

• Machine deep learning classifies vocie disorders

  Accuracy

  May 6,2022-December 30,2023

• Machine deep learning classifies vocie disorders witn multimodality

  precision

  January 1,2024-December 30,2024

• Machine deep learning classifies pathological voice change in Laryngeal Cancer

  precision

  January 1,2024-December 30,2025

Secondary Outcomes (1)

• Machine deep learning classifies vocie disorders witn multimodality

  January 1,2024-December 30,2025

Eligibility Criteria

• Age: 20 Years - 80 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

In this study, 490 patients with voice disorders (including laryngeal cancer, laryngeal precancerous lesions, and benign laryngeal lesions) and 50 healthy people were collected from stroboscopic laryngoscopy videos and vowel audio recordings. Gender, course of disease, VHI and other clinical data.

You may qualify if:

• Laryngeal cancer, laryngeal precancerous lesions, benign laryngeal lesions with voice disorders, healthy people without throat diseases

You may not qualify if:

• A history of laryngeal surgery
• Patients with voice disorders caused by various causes except laryngeal cancer, laryngeal precancerous lesions, and benign laryngeal lesions
• 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;

Sponsors & Collaborators

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: lead
• Duke Kunshan University: collaborator

Related Publications (12)

• 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

  BACKGROUND

• 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.

  PMID: 30316551 BACKGROUND

• 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.

  PMID: 26992554 BACKGROUND

• .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

  BACKGROUND

• 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.

  PMID: 29567049 BACKGROUND

• 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

  BACKGROUND

• 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.

  PMID: 25371410 BACKGROUND

• 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.

  PMID: 31594753 BACKGROUND

• 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.

  PMID: 33113785 BACKGROUND

• 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.

  PMID: 14765711 BACKGROUND

• 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.

  PMID: 32068890 BACKGROUND

• 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.

  PMID: 28008619 RESULT

MeSH Terms

Conditions

Voice Disorders; Speech

Condition Hierarchy (Ancestors)

Laryngeal Diseases; Respiratory Tract Diseases; Otorhinolaryngologic Diseases; Neurologic Manifestations; Nervous System Diseases; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Verbal Behavior; Communication; Behavior

Study Officials

• YueXin Cai

  Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

  STUDY CHAIR

Central Study Contacts

YueXin Cai

CONTACT

13825063663; caiyx25@mail.sysu.edu.cn

Wenting Deng

CONTACT

15017556968; dengwt23@mail.sysu.edu.cn

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 19, 2022
• First Posted: April 27, 2022
• Study Start: May 6, 2022
• Primary Completion: December 30, 2025
• Study Completion (Estimated): February 20, 2027
• Last Updated: April 27, 2022

Record last verified: 2022-03