Multimodal deep learning for Alzheimer’s disease dementia assessment

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these…

• MJ
  Michael J. Fox Foundation for Parkinson's Research
• AH
  American Heart Association

  Award: 20SFRN35460031
• AD
  Alzheimer's Drug Discovery Foundation
• D
  Demensförbundet
• LB
  Lewy Body Dementia Association
• KT
  Karen Toffler Charitable Trust
• NI
  National Institutes of Health

  Award: R01-HL159620