Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
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
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.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Normal group |
|
| |
| Residue Group | - As a result of VFSS (video fluoroscopic swallowing study), the subjects who have residues left around the pharynx or airway |
| |
| Aspiration Group | - VFSS (video fluoroscopic swallowing study) test result, the subjects who have aspiration in the airway |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Voice recording before and after dietary intake | Diagnostic Test |
|
| 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 |
| 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. |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
Normal Group
Residue Group
1) Among those scheduled for VFSS examination due to symptoms of dysphagia, patients who had residues left in the pharynx or airway as a result of reading
Aspiration
1) Among those scheduled for VFSS examination due to symptoms of dysphagia, aspiration occurred in the airway as a result of reading
No limiting criteria for age and underlying disease were established in all groups.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Juseok Ryu, M.D. PhD | Contact | +82-31-787-7739 | jseok337@snu.ac.kr | |
| sunyoung Choi, M.D | Contact | +82-5374-6130 | 0_1235@naver.com |
| Name | Affiliation | Role |
|---|---|---|
| Juseok Ryu, M.D. PhD | Seoul National University Bundang Hospital | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Rehabilitation Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine | Recruiting | Seongnam-si | Gyeonggi-do | 463-707 | South Korea |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 42168445 | Derived | Kim JM, Kim MS, Choi SY, Kim HJ, Ryu JS. Post-swallowing voice-based aspiration screening in dysphagia using a deep learning approach: insights from audio segmentation. Sci Rep. 2026 May 21. doi: 10.1038/s41598-026-53618-w. Online ahead of print. | |
| 39157445 | Derived | Kim JM, Kim MS, Choi SY, Lee K, Ryu JS. A deep learning approach to dysphagia-aspiration detecting algorithm through pre- and post-swallowing voice changes. Front Bioeng Biotechnol. 2024 Aug 2;12:1433087. doi: 10.3389/fbioe.2024.1433087. eCollection 2024. |
Not provided
Not provided
| Type | Date | Date Unknown |
|---|---|---|
| Release | Feb 23, 2026 | |
| Unrelease | Feb 24, 2026 | |
| Release | Feb 24, 2026 | |
| Reset | Mar 16, 2026 |
Not provided
Not provided
| Release Date | Unrelease Date | Unrelease Date Unknown | Reset Date | MCP Release Number |
|---|---|---|---|---|
| Feb 23, 2026 | Feb 24, 2026 | |||
| Feb 24, 2026 |
| ID | Term |
|---|---|
| D003680 | Deglutition Disorders |
| ID | Term |
|---|---|
| D004935 | Esophageal Diseases |
| D005767 | Gastrointestinal Diseases |
| D004066 | Digestive System Diseases |
| D010608 | Pharyngeal Diseases |
Not provided
Not provided
Not provided
Not provided
Not provided
|
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 |
|
| 38555417 | Derived | Kim JM, Kim MS, Choi SY, Ryu JS. Prediction of dysphagia aspiration through machine learning-based analysis of patients' postprandial voices. J Neuroeng Rehabil. 2024 Mar 30;21(1):43. doi: 10.1186/s12984-024-01329-6. |
| Mar 16, 2026 |
| D010038 | Otorhinolaryngologic Diseases |