An observational study in Deglutition Disorders and Neurological Disorder, sponsored by Istituti Clinici Scientifici Maugeri SpA. Recruiting at 2 sites in Italy. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2025-02-20.
Sponsored by Istituti Clinici Scientifici Maugeri SpA · Observational
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.
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.
710 studies on the registry are indexed under Deglutition Disorders; 219 are open to participants now.
This study's planned enrollment of 400 is above the median of 97 across 207 observational studies indexed under Deglutition Disorders.
Browse Deglutition Disorders studies →Istituti Clinici Scientifici Maugeri SpA is the lead sponsor of 113 studies on the registry; 46 are open to participants now.
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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
Exclusion Criteria:
A classification algorithm to screen swallowing disorders in neurological patients
Development of a classification algorithm for dysphagia screening in neurological patients using voice analysis
Time frame: Baseline
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