Automatic Voice Analysis for Dysphagia Screening in Neurological Patients
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Deglutition Disorders, Neurological Disorder. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Italy
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
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.
Primary outcome measures
- A classification algorithm to screen swallowing disorders in neurological patients [Time frame: Baseline]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Case-only
Study locations
Italy · 2 centers
- Istituti Clinici Scientifici Maugeri — Lissone
- Istituti Clinici Scientifici Maugeri — Milan
Identifiers
NCT: NCT06219200 · CE2708