Establishment of Voice Analysis Cohort for Development of Monitoring Technology for Dysphagia
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 300
- 试验地点
- 1
- 主要终点
- Accuracy of machine learning prediction model using voice change before and after dietary intake
研究概览
简要总结
Collection of basic data to develop a technique for monitoring the state of dysphagia using voice analysis.
详细描述
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Design: Prospective study
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Inclusion criteria of the patient group
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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)
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Patients who can record voices such as "Ah for 5 seconds", "Ah. Ah. Ah.", "umm~~~"
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Inclusion criteria of the control group: Patients unable to speak, Patients who cannot follow along, If the VFSS test is a retest
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Setting: Hospital rehabilitation department
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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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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)
排除标准
- •Patients who cannot speak.
- •Patients who cannot speak according to the researcher's instructions.
- •Patients whose VFSS test was reexamined
结局指标
主要结局
Accuracy of machine learning prediction model using voice change before and after dietary intake
时间窗: day 1
Accuracy measures how well machine learning predicts three groups ('Normal', 'Residue', 'Aspiration') according to voice changes before and after dietary intake.
次要结局
- AUC (Area Under the ROC curve) of machine learning prediction model using voice change before and after dietary intake.(day 1)
- mAP (mean Average Precision) of machine learning prediction model using voice change before and after dietary intake(day 1)
- Recall of machine learning prediction model using voice change before and after dietary intake.(day 1)
- Accuracy of machine learning prediction model using only voice after dietary intake.(day 1)
- mAP (mean Average Precision) of machine learning prediction model using only voice after dietary intake.(day 1)
- AUC (Area Under the ROC curve) of machine learning prediction model using only voice after dietary intake.(day 1)
- Recall of machine learning prediction model using only voice after dietary intake.(day 1)
研究者
Ju Seok Ryu
Professor
Seoul National University Hospital