Artificial Intelligence in Diagnosing Dysphagia Patients

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.

Completed

Looking for future studies?

Notify Me

Key information

Conditions

Respiration Disorders Aspiration Pneumonia Aspiration; Liquids Brain Diseases Cardiovascular Diseases Central Nervous System Diseases Cerebrovascular Disorders Deglutition Disorders Digestive System Diseases Dysphonia Esophageal Diseases Gastrointestinal Diseases Infections Laryngeal Diseases Lung Diseases Nervous System Diseases Neurologic Manifestations Otorhinolaryngologic Diseases Pathological Conditions, Signs and Symptoms Pharyngeal Diseases Phonation Disorder Pneumonia Pneumonia, Aspiration Respiratory Tract Diseases Respiratory Tract Infections Signs and Symptoms Stroke Swallowing Disorder Vascular Diseases Voice Disorders

Age range

19 year–90 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Department of Rehabilitation Medicine Bucheon St Mary's Hospital, Catholic University of Korea, College of Medicine

Bucheon-si, Kyounggido, South Korea

About this study

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.

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Inclusion criteria

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

Exclusion criteria

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

Treatment and study plan

Acoustic features (from signals obtained during phonation)

Other

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

Primary outcomes

Functional Oral Intake Scale

Time frame: during the intervention

Dysphagia severity as measured by the the Functional Oral Intake Scale obtained from standardized swallowing tests
Cough strength

Time frame: during the intervention

Spirometry values : cough strength as measured by the spirometric values during voluntary cough

Sponsors and collaborators

Lead sponsor

The Catholic University of Korea

Other

Registry information

Official study title

Classification of Dysphagia Patients at Risk of Aspiration Pneumonia Using Machine Learning Algorithms Incorporating Acoustic Features From Phonetic Evaluation

Important dates

Study start: 2019
Primary completion: 2021
Study completion: 2021
First posted: Oct 28, 2021
Registry last updated: Oct 28, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed