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 volunteersQualifiers
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
Case-only
Retrospective
Treatments tested in this trial
Not listed
Trial groups
Trial outcomes
Primary outcomes
Machine deep learning classifies vocie disorders
Accuracy
Machine deep learning classifies vocie disorders witn multimodality
precision
Machine deep learning classifies pathological voice change in Laryngeal Cancer
precision
Secondary outcomes
Machine deep learning classifies vocie disorders witn multimodality
recall
Locations
Sponsors and contacts
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Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Lead sponsor
Duke Kunshan University
Collaborator
This trial is not recruiting at the moment. You can still explore other options: