A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease.

Wang C, Tachimori H, Yamaguchi H, Sekiguchi A, Li Y, Yamashita Y, for Alzheimer’s Disease Neuroimaging Initiative

Open source

DOI
10.1038/s41398-024-02819-w
Published
2024 Feb 21
Container
Translational psychiatry
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.1038/s41398-024-02819-w,
  title = {A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease.},
  author = {Wang C and Tachimori H and Yamaguchi H and Sekiguchi A and Li Y and Yamashita Y and for Alzheimer’s Disease Neuroimaging Initiative},
  year = {2024},
  journal = {Translational psychiatry},
  doi = {10.1038/s41398-024-02819-w},
  url = {https://doi.org/10.1038/s41398-024-02819-w}
}

RIS

TY  - JOUR
TI  - A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease.
AU  - Wang C
AU  - Tachimori H
AU  - Yamaguchi H
AU  - Sekiguchi A
AU  - Li Y
AU  - Yamashita Y
AU  - for Alzheimer’s Disease Neuroimaging Initiative
PY  - 2024
JO  - Translational psychiatry
DO  - 10.1038/s41398-024-02819-w
UR  - https://doi.org/10.1038/s41398-024-02819-w
ER  -

APA

C, W., H, T., H, Y., A, S., Y, L., Y, Y., & Initiative, F. A. D. N. (2024). A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease.. Translational psychiatry. https://doi.org/10.1038/s41398-024-02819-w

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