Detecting bulbar amyotrophic lateral sclerosis (ALS) using automatic acoustic analysis.

ABSTRACT: Automatic speech assessments have the potential to dramatically improve ALS clinical practice and facilitate patient stratification for ALS clinical trials. Acoustic speech analysis has demonstrated the ability to capture a variety of relevant speech motor impairments, but implementation has been hindered by both the nature of lab-based assessments (requiring travel and time for patients) and also by the opacity of some acoustic feature analysis methods. These challenges and others have obscured the ability to distinguish different ALS disease stages/severities. Validation of automated acoustic analysis tools could enable detection of early signs of ALS, and these tools could be deployed to screen and monitor patients without requiring clinic visits. Here, we sought to determine whether ac

SUBMITTER: Simmatis LER 

PROVIDER: S-EPMC10838438 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature