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Clinical 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. 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.


Clinical Trial 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 Design


Related Conditions & MeSH terms


NCT number NCT05348031
Study type Observational
Source Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Contact YueXin Cai
Phone 13825063663
Email caiyx25@mail.sysu.edu.cn
Status Not yet recruiting
Phase
Start date May 6, 2022
Completion date February 20, 2027

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