Nature Communications (Jun 2022)

Multimodal deep learning for Alzheimer’s disease dementia assessment

  • 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

DOI
https://doi.org/10.1038/s41467-022-31037-5
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 17

Abstract

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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.