AI-driven fusion of multimodal data for Alzheimer’s disease biomarker assessment
Abstract Alzheimer’s disease (AD) diagnosis hinges on detecting amyloid beta (Aβ) plaques and neurofibrillary tau (τ) tangles, typically assessed using PET imaging. While accurate, these modalities are expensive and not widely accessible, limiting their utility in routine clinical practice. Here, we...
Saved in:
| Main Authors: | Varuna H. Jasodanand, Sahana S. Kowshik, Shreyas Puducheri, Michael F. Romano, Lingyi Xu, Rhoda Au, Vijaya B. Kolachalama |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62590-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Novel hybrid intelligence model for early Alzheimer's diagnosis utilizing multimodal biomarker fusion
by: Shehu Mohammed, et al.
Published: (2025-01-01) -
Temporal association of neuropsychological test performance using unsupervised learning reveals a distinct signature of Alzheimer's disease status
by: Prajakta S. Joshi, et al.
Published: (2019-01-01) -
Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer’s disease diagnosis and biomarker identification
by: Shan Huang, et al.
Published: (2025-12-01) -
Assisted Diagnosis of Alzheimer’s Disease Based on Deep Learning and Multimodal Feature Fusion
by: Yu Wang, et al.
Published: (2021-01-01) -
AD-Diff: enhancing Alzheimer's disease prediction accuracy through multimodal fusion
by: Lei Han
Published: (2025-03-01)