Deglutition Disorders
Conditions
Brief summary
Collection of basic data to develop a technique for monitoring the state of dysphagia using voice analysis.
Detailed description
* Design: Prospective study * Inclusion criteria of the patient group * Patients scheduled for VFSS examination and normal person (without dysphagia) capable of recording voice (selected as a control group for comparison of voice indicators with patients with dysphagia) * Patients who can record voices such as Ah for 5 seconds, Ah. Ah. Ah., umm\ \ \ * Inclusion criteria of the control group: Patients unable to speak, Patients who cannot follow along, If the VFSS test is a retest * Setting: Hospital rehabilitation department * Intervention: After obtaining the consent form for the patient scheduled for the VFSS test, Ah for 5 seconds, after clearing the throat, Ah for 5 seconds, briefly cut with a high-pitched sound, Ah. Ah. Ah, close your lips lightly and make a ummm\ \ \ \ sound, and record 2 times each.
Interventions
DIAGNOSTIC_TESTVoice recording before and after dietary intake
* A person who is scheduled to undergo a VFSS test, and his/her voice is recorded before and after eating for the VFSS test * For general subjects, only voice recordings were conducted before and after food/water intake without a VFSS test.
Sponsors
Seoul National University Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
* Patients with dysphagia and scheduled for VFSS testing * Patients who can record voice such as Ah for 5 seconds, Ah. ah. ah, or Um\ \ * Normal people (without dysphagia symptoms) who can record voice (additionally recruited for comparison of voice indicators with patients with dysphagia)
Exclusion criteria
* Patients who cannot speak. * Patients who cannot speak according to the researcher's instructions. * Patients whose VFSS test was reexamined
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of machine learning prediction model using voice change before and after dietary intake | day 1 | Accuracy measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice changes before and after dietary intake. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Recall of machine learning prediction model using voice change before and after dietary intake. | day 1 | Recall measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice changes before and after dietary intake. |
| AUC (Area Under the ROC curve) of machine learning prediction model using voice change before and after dietary intake. | day 1 | AUC measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice changes before and after dietary intake. |
| Accuracy of machine learning prediction model using only voice after dietary intake. | day 1 | Accuracy measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice only voice after dietary intake. |
| mAP (mean Average Precision) of machine learning prediction model using voice change before and after dietary intake | day 1 | mAP measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice changes before and after dietary intake. |
| Recall of machine learning prediction model using only voice after dietary intake. | day 1 | Recall measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice only voice after dietary intake. |
| AUC (Area Under the ROC curve) of machine learning prediction model using only voice after dietary intake. | day 1 | AUC measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice only voice after dietary intake. |
| mAP (mean Average Precision) of machine learning prediction model using only voice after dietary intake. | day 1 | mAP measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice only voice after dietary intake. |
Countries
South Korea
Contacts
Primary ContactJuseok Ryu, M.D. PhD
Backup Contactsunyoung Choi, M.D
Outcome results
None listed