A proposed machine learning architecture for the subtyping, diagnosis, and characterization of Frontaltemporal Dementia (FTD)
Further model tuning for the multi-class model is most needed; however, these preliminary results suggest that GMLVQ trajectory modeling shows promise for advancing the diagnosis and assessment of FTD.
Authors
- Liz Yuanxi Lee (ORCID: https://orcid.org/0000-0002-6643-6312)
- Zoe Kourtzi (ORCID: https://orcid.org/0000-0001-9441-7832)
- Marcella Montagnese (ORCID: https://orcid.org/0000-0001-5467-0104)
- Ethan Wong
- Timothy Rittman
Institutions
- University of Cambridge (GB)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70856_103689
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00