Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT06219200 |
| Other study ID # |
CE2708 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
October 23, 2023 |
| Est. completion date |
December 31, 2026 |
Study information
| Verified date |
December 2023 |
| Source |
Istituti Clinici Scientifici Maugeri SpA |
| Contact |
Beatrice De Maria, PhD |
| Phone |
0250725 |
| Email |
beatrice.demaria[@]icsmaugeri.it |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial 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.
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.
