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Not yet recruitingNCT05348031Updated Apr 27, 2022

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

An observational study in Voice Disorders, sponsored by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University. Not yet recruiting. Open to participants aged 20 Years to 80 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2022-04-27.

Sponsored by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University · Observational

From the registry’s dates

  • Primary completion was expected by Dec 2025, 10 months ago, but the record still lists the study as not yet recruiting.
Study type
Observational
Model
Case-only
Time perspective
Retrospective
Enrollment
1
Ages
20 Years to 80 Years
Sex
All
01

Study 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.

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
02

Conditions studied

  • Voice Disorders

Keywords

  • deep learning
  • multimodality
  • structural voice disorders
  • stroboscopic laryngoscope video
  • Speech
03

In context

Voice Disorders

77 studies on the registry are indexed under Voice Disorders; 17 are open to participants now.

This study's planned enrollment of 1 is below the median of 100 across 23 observational studies indexed under Voice Disorders.

Browse Voice Disorders studies →

Lead sponsor

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University is the lead sponsor of 483 studies on the registry; 283 are open to participants now.

Browse Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University studies →

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
20 Years to 80 Years
Sexes eligible
All
Accepts healthy volunteers
Yes
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.

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

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

Exclusion criteria

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;
05

Study design

Observational model
Case-only
Time perspective
Retrospective
Enrollment
1 participant (estimated)
Patient registry
No
06

What researchers measure

Primary outcomes

  1. Machine deep learning classifies vocie disorders

    Accuracy

    Time frame: May 6,2022-December 30,2023

  2. Machine deep learning classifies vocie disorders witn multimodality

    precision

    Time frame: January 1,2024-December 30,2024

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

    precision

    Time frame: January 1,2024-December 30,2025

Secondary outcomes

  1. Machine deep learning classifies vocie disorders witn multimodality

    recall

    Time frame: January 1,2024-December 30,2025

07

Study locations

No study locations are listed for this record.

08

References and documents

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 ↗

Individual participant data

Plan to share: Undecided

09

Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reached

Start is estimated for May 2022; primary completion for Dec 2025, 43 months later.

How these figures are worked out, and which studies are left out of them.

Registered
Apr 19, 2022
First posted
Apr 27, 2022
StartEst.
May 6, 2022
Primary completionEst.
Dec 30, 2025
CompletionEst.
Feb 20, 2027
Last update
Apr 27, 2022
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on Apr 27, 2022, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT05348031
Lead sponsor
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Collaborators
Duke Kunshan University
Responsible party
Sponsor
First posted
Apr 27, 2022
Start date
May 6, 2022 (estimated)
Primary completion
Dec 30, 2025 (estimated)
Completion
Feb 20, 2027 (estimated)
Last update
Apr 27, 2022

Study contacts

YueXin Cai
Contact
caiyx25@mail.sysu.edu.cn
13825063663
Wenting Deng
Contact
dengwt23@mail.sysu.edu.cn
15017556968
YueXin Cai
study chair · Sun Yat-sen Memorial Hospital,Sun Yat-sen University

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is not yet recruiting, as verified in Mar 2022. You cannot join it, but the record below documents what was studied.

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Discussion

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