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
Classification of Dysphagia Patients at Risk of Aspiration Pneumonia Using Machine Learning Algorithms Incorporating Acoustic Features From Phonetic Evaluation
Sponsor study identifier
observational
449
1 country
1
Study Start
September 1, 2019
Actual (reported)Primary Completion
September 1, 2021
Actual (reported)Study Completion
October 1, 2021
Actual (reported)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.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
October 1, 2021
Actual (reported)Click on a node to explore related trials.
Some registry-reported names shown below are not yet linked in this graph.
Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
September 1, 2019
Actual (reported)September 1, 2021
Actual (reported)October 1, 2021
Actual (reported)October 1, 2021
Actual (reported)October 28, 2021
Actual (reported)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
Able to start oral feeding after assessment
Non oral feeding and high risk of aspiration
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
Registry-reported names are shown in full. Names without a link have not yet been matched to a Syfrah profile.
First ever stroke patients referred for swallowing disorders
Department of Rehabilitation Medicine Bucheon St Mary's Hospital, Catholic University of Korea, College of Medicine
Bucheon-si, Kyounggido, South Korea
Sun Im, MD PhD
The Catholic University of Korea
449 participants (actual enrollment reported by the registry).
First Submitted
October 1, 2021
First Posted
October 28, 2021
Study Start
September 1, 2019
Actual (reported)Development and Validation of an Online Independent Training Program for TOR-BSST© Dysphagia Screeners
Contact the study team to confirm eligibility.
Primary Completion
September 1, 2021
Actual (reported)Study Completion
October 1, 2021
Actual (reported)Last Updated
October 28, 2021
Record last verified: 2021-10
Data would be accessible only upon formal request to the formal PI
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Source: ClinicalTrials.gov.
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