Abstract
AbstractBulbar deterioration in amyotrophic lateral sclerosis (ALS) is a devastating characteristic that impairs patients’ ability to communicate, and is linked to shorter survival. The existing clinical instruments for assessing bulbar function lack sensitivity to early changes. In this paper, using a cohort of N = 65 ALS patients who provided regular speech samples for 3–9 months, we demonstrated that it is possible to remotely detect early speech changes and track speech progression in ALS via automated algorithmic assessment of speech collected digitally.
Funder
U.S. Department of Health & Human Services | National Institutes of Health
National Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
Health Information Management,Health Informatics,Computer Science Applications,Medicine (miscellaneous)
Cited by
48 articles.
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