| Autor: |
Shangran Qiu, Matthew I. Miller, Prajakta S. Joshi, Joyce C. Lee, Chonghua Xue, Yunruo Ni, Yuwei Wang, Ileana De Anda-Duran, Phillip H. Hwang, Justin A. Cramer, Brigid C. Dwyer, Honglin Hao, Michelle C. Kaku, Sachin Kedar, Peter H. Lee, Asim Z. Mian, Daniel L. Murman, Sarah O’Shea, Aaron B. Paul, Marie-Helene Saint-Hilaire, E. Alton Sartor, Aneeta R. Saxena, Ludy C. Shih, Juan E. Small, Maximilian J. Smith, Arun Swaminathan, Courtney E. Takahashi, Olga Taraschenko, Hui You, Jing Yuan, Yan Zhou, Shuhan Zhu, Michael L. Alosco, Jesse Mez, Thor D. Stein, Kathleen L. Poston, Rhoda Au, Vijaya B. Kolachalama |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Nature Communications, Vol 13, Iss 1, Pp 1-17 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-022-31037-5 |
| Popis: |
Here the authors present a deep learning framework for dementia diagnosis, which can identify persons with normal cognition, mild cognitive impairment, Alzheimer’s disease, and dementia due to other etiologies. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink