Artificial Intelligence in Diagnosing Dysphagia Patients

Phase

N/A

Condition

Stroke

Pneumonia

Cerebral Ischemia

Treatment

N/A

Clinical Study ID

NCT05098808

HC19EESE0060

Ages 19-90
All Genders

Study 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.

Eligibility Criteria

Inclusion

Inclusion Criteria:

Inclusion criteria

Suspected swallowing disorder who were referred for swallowing assessment
Dysphagia attributable to brain lesion including stroke

Exclusion

Exclusion Criteria:

Participants who were unable to perform phonation
Participants who had no VFSS or standardized swallowing assessment results
Participants with no spirometric measurements

Study Design

September 01, 2019

October 01, 2021

Study Description

This study was prospective study, and patients who visited the department of rehabilitation medicine in a single university-affiliated tertiary hospital with dysphagic symptoms from September 2019 to March 2021 were included.Voice recording was performed at the enrollment with blinded assessment, where the participants first visited the rehabilitation department with chief complaints of dysphagia. The cough sounds were recorded with an iPad (Apple, Cupertino, CA, USA) through an embedded microphone.

From the acoustic files we extracted fourteen voice parameters that include the average value and standard deviation of the fundamental frequency (f0), harmonic-to-noise ratio (HNR), the jitter that refers to frequency instability, and the shimmer that represents the amplitude instability of the sound signal.

Machine learning algorithms and sophisticated deep neural network analysis will be performed.

Connect with a study center

Department of Rehabilitation Medicine Bucheon St Mary's Hospital, Catholic University of Korea, College of Medicine
Bucheon, Kyounggido
Korea, Republic of
Site Not Available

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