Collection of basic data to develop a technique for monitoring the state of dysphagia using voice analysis.
* 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 Type
OBSERVATIONAL
Enrollment
300
* 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.
Department of Rehabilitation Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine
Seongnam-si, Gyeonggi-do, South Korea
RECRUITINGAccuracy 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.
Time frame: day 1
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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
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Time frame: 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.
Time frame: 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.
Time frame: day 1