Improving care for amyotrophic lateral sclerosis with artificial intelligence and affective computing
This study introduces a novel approach to linking pulse and speech analysis to evaluate emotional responses in ALS patients. AI and affective computing provide valuable insights into emotional responses and disease progression, with potential applications for other neurological disorders. This approach could augment clinical trial outcomes by offering a more comprehensive view of patient well-being.
Authors
- Quentin Lesport (ORCID: https://orcid.org/0009-0009-0039-7064)
- Gülşen Öztosun (ORCID: https://orcid.org/0000-0002-1638-9095)
- Henry J. Kaminski (ORCID: https://orcid.org/0000-0002-8195-0141)
- Marc Garbey (ORCID: https://orcid.org/0000-0001-7898-9136)
- Elham Bayat (ORCID: https://orcid.org/0000-0003-0502-7870)
- Veda Ghodasara
Institutions
- Centre National de la Recherche Scientifique (FR)
- George Washington University (US)
- Laboratoire des Sciences de l'Ingénieur pour l'Environnement (FR)
- GW Medical Faculty Associates (US)
Publication Details
- Journal
- Journal of the Neurological Sciences
- Published
- 2024-11-25
- DOI
- https://doi.org/10.1016/j.jns.2024.123328
- Citations
- 5
- Primary Topic
- Stroke Rehabilitation and Recovery
- Type
- article
- Field-Weighted Citation Impact
- 1.25
Funders
- Roche
- George Washington University
- Takeda Pharmaceuticals U.S.A.
- National Institutes of Health