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

Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age20-80
SponsorSun Yat-Sen Memorial Hospital of Sun Yat-Sen University

About this trial

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.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

None

Disqualifiers

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 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.

Trial design

Design model

Case-only

Time perspective

Retrospective

Treatments tested in this trial

Not listed

Trial groups

No trial groups listed

Trial outcomes

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

Other outcomes

Locations

This trial has no locations.

Sponsors and contacts

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