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Voice DisordersNCT05348031

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

An observational study of Voice Disorders, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.

Not yet recruiting

Opening soon. The study is approved but enrolment has not started yet. Sites may already take enquiries.

Sites and contacts
Official record
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Trial 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. Read more about this study

Who is it for?
Ages 20 to 80 yearsHealthy volunteers can take part
Conditions the study is for
Voice Disorders
How many people
1 participants
Where
No sites listed yet
Study dates
6 May 2022to 20 Feb 2027
Phase
Observational
Are there results?
Not yet: the study is under way

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 20 to 80 years
  • All sexes
  • Healthy volunteers accepted
  • Adult, Older adult

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.

Who may be able to join

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

Who cannot join

  • 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;

Trial locations and contacts

No sites are listed yet. Contact the study team or a site near you to ask about taking part.

YueXin Cai contact

13825063663

caiyx25@mail.sysu.edu.cn

Wenting Deng contact

15017556968

dengwt23@mail.sysu.edu.cn

About this study

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.

Read the 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
Conditions
Voice Disorders
Keywords
deep learning, multimodality, structural voice disorders, stroboscopic laryngoscope video, Speech
Medical subject headings
Voice Disorders, Speech

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Machine deep learning classifies vocie disorders

    Accuracy

    Measured: May 6,2022-December 30,2023

  2. Machine deep learning classifies vocie disorders witn multimodality

    precision

    Measured: January 1,2024-December 30,2024

  3. Machine deep learning classifies pathological voice change in Laryngeal Cancer

    precision

    Measured: January 1,2024-December 30,2025

Secondary outcomes (1)

  1. Machine deep learning classifies vocie disorders witn multimodality

    recall

    Measured: January 1,2024-December 30,2025

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted27 Apr 2022
  2. Study start6 May 2022 (estimated)
  3. Primary completion30 Dec 2025 (estimated)
  4. Study completion20 Feb 2027 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Case only
Time perspective
Retrospective
Enrolment
1 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT05348031
Sponsor's protocol ID
SYSEC-KY-KS-2022-040
Status last verified
Mar 2022
First submitted
19 Apr 2022
Last updated
27 Apr 2022
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
Undecided

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.

    PubMed 30316551

  • 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.

    PubMed 26992554

  • .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.

    PubMed 29567049

  • 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.

    PubMed 25371410

  • 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.

    PubMed 31594753

  • 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.

    PubMed 33113785

  • 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.

    PubMed 14765711

  • 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.

    PubMed 32068890

  • 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.

    PubMed 28008619

Questions about this trial

What is this clinical trial studying?

An observational study of Voice Disorders, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 1 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: opening soon, as of the sponsor's last update on 27 Apr 2022. Status can change between updates, so confirm with the study contact before making plans.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

Contact YueXin Cai (13825063663, caiyx25@mail.sysu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Machine deep learning classifies vocie disorders (time frame: May 6,2022-December 30,2023). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT05348031, which also lists 12 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Apr 2022; AzTrials last refreshed its copy on 2 Nov 2026.

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