Automatic Voice Analysis for Dysphagia Screening in Neurological Patients (VOICED)

The proposed study suggests using automatic voice analysis and machine learning algorithms to develop a dysphagia screening tool for neurological patients. The research involves patients with Parkinson's disease, stroke, and amyotrophic lateral sclerosis, both with and without dysphagia, along with healthy individuals. Participants perform various vocal tasks during a single recording session. Voice signals are analysed and used as input for machine learning classification algorithms. The significance of this study is that oropharyngeal dysphagia, a condition involving swallowing difficulties in the transit of food or liquids from the mouth to the esophagus, generates malnutrition, dehydration, and pneumonia, significantly contributing to management costs and hospitalization durations. Currently, there is a lack of rapid and effective dysphagia screening methods for healthcare personnel, with only expensive invasive tests and clinical scales in use.

Study Overview

• Status: Recruiting
• Conditions: Deglutition Disorders; Neurological Disorder

Detailed Description

Background:

Oropharyngeal dysphagia, defined as any alterations in swallowing abilities during the transit of food or liquids from the oral cavity to the esophagus, is an insidious complication of many neurological diseases. This condition can seriously lead to severe complications such as malnutrition, dehydration, and pneumonia, which overall has a huge impact on management costs and the number of hospitalization days. In this context, it is essential to immediately recognize the risk factors and the first signs of dysphagia to take prompt adequate actions and request further clinical and instrumental evaluations. Rapid, quantitative, and effective dysphagia screening methods are not currently available to support healthcare personnel. To date, only clinical rating scales or expensive invasive tests that require specialized personnel are adopted in clinical scenarios, whereas no objective tools are still available in extra-hospital contexts to alert patients of risk situations.

Current Gaps in Knowledge and Aim:

Since oropharyngeal dysphagia is caused by an impaired coordination control of the swallowing muscles and these muscles play also an important role in the phonation process, investigating voice alterations could be a screening option to recognize dysphagia in patients with neurological diseases. In the current literature, automatic voice analysis and the use of machine learning algorithms have given relevant findings in the discrimination between neurological diseases and healthy subjects, and there are also interesting preliminary data on dysphagia. The goal of this study is to the development a machine learning classification algorithm for dysphagia screening in neurological patients using automatic voice analysis.

Study Involvement:

The study involves patients with neurological diseases (Parkinson's disease, stroke, amyotrophic lateral Sclerosis) with or without dysphagia and healthy individuals. The participants are asked to perform some vocal tasks (sustained vocal phonation, diadochokinetic tasks, production of standardized sentences, free speech) in a single experimental session at the enrolment. Voice recordings will be automatically proceeded to derive acoustic voice features, used as input for the machine learning classification algorithm. The evaluation of the participants to characterize the studied sample is carried out with the collection of anamnestic and clinical data.

• Study Type: Observational
• Enrollment (Estimated): 400

Contacts and Locations

• Study Contact: Name: Beatrice De Maria, PhD; Phone Number: +39 0250725; Email: beatrice.demaria@icsmaugeri.it

Study Locations

• Italy
  • Lombardia
    • Lissone, Lombardia, Italy
      • Recruiting
      • Istituti Clinici Scientifici Maugeri
      • Contact: Beatrice De Maria
    • Milan, Lombardia, Italy
      • Recruiting
      • Istituti Clinici Scientifici Maugeri
      • Contact: Beatrice De Maria, PhD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

The study population included a group of patients with Parkinson's disease, a group of post-stroke patients, a group of patients with amyotrophic lateral sclerosis, and a group of healthy individuals

Description

Inclusion Criteria:

• Patients with a diagnosis of stroke, Parkinson's disease, or amyotrophic lateral sclerosis, or healthy individuals.
• Age higher than 18 years old.

Exclusion Criteria:

• Cognitive impairment that do not allow participants to understand the requested vocal tasks.
• Ear, nose,throat diseases and other disorders able to affect voice quality.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
A classification algorithm to screen swallowing disorders in neurological patients	Development of a classification algorithm for dysphagia screening in neurological patients using voice analysis	Baseline

Collaborators and Investigators

• Sponsor: Istituti Clinici Scientifici Maugeri SpA
• Collaborators: Politecnico di Milano

Study record dates

Study Major Dates

• Study Start (Actual): October 23, 2023
• Primary Completion (Estimated): October 23, 2026
• Study Completion (Estimated): December 31, 2026

Study Registration Dates

• First Submitted: January 12, 2024
• First Submitted That Met QC Criteria: January 12, 2024
• First Posted (Actual): January 23, 2024

Study Record Updates

• Last Update Posted (Actual): January 23, 2024
• Last Update Submitted That Met QC Criteria: January 12, 2024
• Last Verified: December 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Digestive System Diseases; Gastrointestinal Diseases; Pharyngeal Diseases; Otorhinolaryngologic Diseases; Esophageal Diseases; Nervous System Diseases; Deglutition Disorders
• Other Study ID Numbers: CE2708

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No