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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Voice Disorders

Detailed Description

1. Detection and Classification of Acoustic Lesions Based on Speech Deep Learning
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

• Study Type: Observational
• Enrollment (Anticipated): 1

Contacts and Locations

• Study Contact: Name: YueXin Cai; Phone Number: 13825063663; Email: caiyx25@mail.sysu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 20 years to 80 years (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• 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.

Description

Inclusion Criteria:

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

Exclusion Criteria:

1. A history of laryngeal surgery
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;

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Machine deep learning classifies vocie disorders witn multimodality	recall	January 1,2024-December 30,2025

Collaborators and Investigators

• Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
• Collaborators: Duke Kunshan University
• Investigators: Study Chair: YueXin Cai, Sun Yat-sen Memorial Hospital,Sun Yat-sen University

Publications and helpful links

General Publications

• 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.

Study record dates

Study Major Dates

• Study Start (ANTICIPATED): May 6, 2022
• Primary Completion (ANTICIPATED): December 30, 2025
• Study Completion (ANTICIPATED): February 20, 2027

Study Registration Dates

• First Submitted: April 19, 2022
• First Submitted That Met QC Criteria: April 22, 2022
• First Posted (ACTUAL): April 27, 2022

Study Record Updates

• Last Update Posted (ACTUAL): April 27, 2022
• Last Update Submitted That Met QC Criteria: April 22, 2022
• Last Verified: March 1, 2022

More Information

Terms related to this study

• Keywords: Speech; deep learning; multimodality; structural voice disorders; stroboscopic laryngoscope video
• Additional Relevant MeSH Terms: Pathologic Processes; Nervous System Diseases; Respiratory Tract Diseases; Neurologic Manifestations; Otorhinolaryngologic Diseases; Laryngeal Diseases; Disease; Voice Disorders
• Other Study ID Numbers: SYSEC-KY-KS-2022-040

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No