Artificial Intelligence in Diagnosing Dysphagia Patients
Classification of Dysphagia Patients at Risk of Aspiration Pneumonia Using Machine Learning Algorithms Incorporating Acoustic Features From Phonetic Evaluation
1 other identifier
observational
449
1 country
1
Brief Summary
In this prospective study we extracted acoustic parameters using PRAAT from patient's attempt to phonate during the clinical evaluation using a digital smart device. From these parameters we attempted (1) to define which of the PRAAT acoustic features best help to discriminate patients with dysphagia (2) to develop algorithms using sophisticated ML techniques that best classify those i) with dysphagia and those ii ) at high risk of respiratory complications due to poor cough force.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Sep 2019
Typical duration for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
Study Start
First participant enrolled
September 1, 2019
CompletedPrimary Completion
Last participant's last visit for primary outcome
September 1, 2021
CompletedFirst Submitted
Initial submission to the registry
October 1, 2021
CompletedStudy Completion
Last participant's last visit for all outcomes
October 1, 2021
CompletedFirst Posted
Study publicly available on registry
October 28, 2021
CompletedOctober 28, 2021
October 1, 2021
2 years
October 1, 2021
October 18, 2021
Conditions
Outcome Measures
Primary Outcomes (2)
Functional Oral Intake Scale
Dysphagia severity as measured by the the Functional Oral Intake Scale obtained from standardized swallowing tests
during the intervention
Cough strength
Spirometry values : cough strength as measured by the spirometric values during voluntary cough
during the intervention
Study Arms (2)
Dysphagia mild
Able to start oral feeding after assessment
Dysphagia severe
Non oral feeding and high risk of aspiration
Interventions
Acoustic features will be obtained via phonation files. A voice recorder application provided by Apple was used, and the sampling frequency of the sound was 44,100 Hz. The digitized cough sound signals were band-pass-filtered between 20 to 16,000 Hz to use data from the whole frequency band gathered by the iPad. In each case, the smart device was positioned 20cm from the patient
Eligibility Criteria
First ever stroke patients referred for swallowing disorders
You may qualify if:
- Suspected swallowing disorder who were referred for swallowing assessment
- Dysphagia attributable to brain lesion including stroke
You may not qualify if:
- Participants who were unable to perform phonation
- Participants who had no VFSS or standardized swallowing assessment results
- Participants with no spirometric measurements
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Department of Rehabilitation Medicine Bucheon St Mary's Hospital, Catholic University of Korea, College of Medicine
Bucheon-si, Kyounggido, South Korea
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Sun Im, MD PhD
The Catholic University of Korea
Study Design
- Study Type
- observational
- Observational Model
- CASE CONTROL
- Time Perspective
- PROSPECTIVE
- Target Duration
- 7 Days
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- associate professor
Study Record Dates
First Submitted
October 1, 2021
First Posted
October 28, 2021
Study Start
September 1, 2019
Primary Completion
September 1, 2021
Study Completion
October 1, 2021
Last Updated
October 28, 2021
Record last verified: 2021-10
Data Sharing
- IPD Sharing
- Will not share
Data would be accessible only upon formal request to the formal PI