Establishment of Voice Analysis Cohort for Development of Monitoring Technology for Dysphagia

Study Overview
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Brief Summary
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Collection of basic data to develop a technique for monitoring the state of dysphagia using voice analysis.

Detailed Description
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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.

Official Title
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Establishment of Voice Analysis Cohort for Development of Monitoring Technology for Dysphagia

Conditions
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Deglutition Disorders

Intervention / Treatment
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* Diagnostic Test: Voice recording before and after dietary intake


Participation Criteria
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Eligibility Criteria
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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
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Normal group<br>A person judged normal among VFSS (video fluoroscopic swallowing study) examiners Normal subjects without symptoms of dysphagia | Diagnostic Test: Voice recording before and after dietary intake<br>* 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.<br>|
| Residue Group<br>- As a result of VFSS (video fluoroscopic swallowing study), the subjects who have residues left around the pharynx or airway | Diagnostic Test: Voice recording before and after dietary intake<br>* 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.<br>|
| Aspiration Group<br>- VFSS (video fluoroscopic swallowing study) test result, the subjects who have aspiration in the airway | Diagnostic Test: Voice recording before and after dietary intake<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Accuracy of machine learning prediction model using voice change before and after dietary intake | Accuracy measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice changes before and after dietary intake. | day 1 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| mAP (mean Average Precision) of machine learning prediction model using voice change before and after dietary intake | mAP measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice changes before and after dietary intake. | day 1 | 
| Recall of machine learning prediction model using voice change before and after dietary intake. | Recall measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice changes before and after dietary intake. | day 1 | 
| AUC (Area Under the ROC curve) of machine learning prediction model using voice change before and after dietary intake. | AUC measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice changes before and after dietary intake. | day 1 | 
| Accuracy of machine learning prediction model using only voice after dietary intake. | Accuracy measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice only voice after dietary intake. | day 1 | 
| mAP (mean Average Precision) of machine learning prediction model using only voice after dietary intake. | mAP measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice only voice after dietary intake. | day 1 | 
| Recall of machine learning prediction model using only voice after dietary intake. | Recall measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice only voice after dietary intake. | day 1 | 
| AUC (Area Under the ROC curve) of machine learning prediction model using only voice after dietary intake. | AUC measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice only voice after dietary intake. | day 1 |