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

Recruiting

• Conditions: Deglutition Disorders

• Registration Number: NCT05149976
• Lead Sponsor: Seoul National University Hospital

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.

Study Timeline

• Study Start: 2021-10-07
• Study First Submitted: 2021-11-01
• Study First Submitted QC: 2021-11-25
• Study First Posted: 2021-12-08
• Status Verified: 2024-04
• Last Update Submitted: 2024-04-18
• Last Update Posted: 2024-04-19
• Primary Completion: 2024-12
• Study Completion: 2024-12

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 300

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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 Outcome Measures

Name	Time	Method
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.
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 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.
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 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.
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.
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.

Trial Locations

Locations (1)

Department of Rehabilitation Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine; 🇰🇷 Seongnam-si, Gyeonggi-do, Korea, Republic of