Artificial intelligence to predict modified rankin score after acute stroke symptoms using wrist-worn triaxial accelerometry data
AI applied to wearable accelerometry predicted short-term mRS score and functional change after stroke with accuracy above chance. Although predictive performance was modest, these results provide proof-of-concept that AI applied to passive wearable data can capture meaningful post-stroke functional variation and could inform future real-time monitoring frameworks. Integrating multimodal wearable and clinical data may further improve prediction of post-stroke functional outcomes.
Authors
- Alexander Gerlach (ORCID: https://orcid.org/0000-0003-1787-1868)
- David S. Liebeskind (ORCID: https://orcid.org/0000-0002-5109-8736)
- Sumita Kohli
- Ari Shechter (ORCID: https://orcid.org/0000-0001-8243-0247)
- Girish N. Nadkarni (ORCID: https://orcid.org/0000-0001-6319-4314)
- Benjamin R. Kummer (ORCID: https://orcid.org/0000-0002-1413-8014)
- Joshua Z. Willey (ORCID: https://orcid.org/0000-0001-5180-2044)
Institutions
- University of California, Los Angeles (US)
- University of the Arts (US)
- Columbia University (US)
- University of Pennsylvania (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Journal of Stroke and Cerebrovascular Diseases
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1016/j.jstrokecerebrovasdis.2025.108508
- Primary Topic
- Stroke Rehabilitation and Recovery
- Type
- article
- Field-Weighted Citation Impact
- 0.00