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Automatic Voice Analysis for Dysphagia Screening in Neurological Patients

NCT06219200 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 400

Last updated 2025-02-20

No results posted yet for this study

Summary

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.

Conditions

  • Deglutition Disorders
  • Neurological Disorder

Sponsors & Collaborators

  • Politecnico di Milano

    collaborator OTHER

  • Istituti Clinici Scientifici Maugeri SpA

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-10-11
Primary Completion
2025-12-31
Completion
2025-12-31

Countries

  • Italy

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06219200 on ClinicalTrials.gov