Driving Performance as a Marker of Cognitive Status: A Machine Learning‐ based Approach
This study demonstrates the feasibility of using machine learning to classify cognitive status based on driving performance. Findings suggest that driving metrics have potential as a tool for detecting cognitive decline. In the future, we will extend this approach to detect pre-clinical cognitive decline in individuals with subjective cognitive decline, assess AI's reliability, and support timely interventions.
Authors
- Alex Mihailidis (ORCID: https://orcid.org/0000-0003-2233-0919)
- Jennifer L. Campos (ORCID: https://orcid.org/0000-0002-7054-3946)
- Gary E Naglie
- Gelareh Hajian (ORCID: https://orcid.org/0000-0001-5758-5204)
- Elaine Stasiulis (ORCID: https://orcid.org/0000-0003-3572-9371)
- Mark Jeffrey Rapoport (ORCID: https://orcid.org/0000-0003-2670-1975)
- Bing Ye
Institutions
- Sunnybrook Health Science Centre (CA)
- University of Toronto (CA)
- Toronto Rehabilitation Institute (CA)
- Health Sciences Centre (CA)
- Baycrest Hospital (CA)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70858_106244
- Primary Topic
- Older Adults Driving Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00