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.

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Key information

Conditions

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

Age range

20 year–80 year

Sex eligibility

All sexes

Study type

Observational

About this study

Detection and Classification of Acoustic Lesions Based on Speech Deep Learning
Detection and Classification of Acoustic Lesions Based on Deep Learning of Images
Detection and Classification of Acoustic Lesions Based on Deep Learning Based on Multimodality

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

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;

Treatment and study plan

Primary outcomes

Machine deep learning classifies vocie disorders

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

Accuracy
Machine deep learning classifies vocie disorders witn multimodality

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

precision
Machine deep learning classifies pathological voice change in Laryngeal Cancer

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

precision

Secondary outcomes

Machine deep learning classifies vocie disorders witn multimodality

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

recall

Study contacts

Contact information is provided by the study sponsor or research team.

Wenting Deng

CONTACT

[email protected]

15017556968

YueXin Cai

CONTACT

[email protected]

13825063663

Sponsors and collaborators

Lead sponsor

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

Other

Collaborators

Duke Kunshan University

Registry information

Important dates

Study start: 2022
Primary completion: 2025
Study completion: 2027
First posted: Apr 27, 2022
Registry last updated: Apr 27, 2022

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

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