Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data

Deep learning, a state-of-the-art machine learning approach, has shown outstanding performance over traditional machine learning in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vision. The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention, as rapid progress in neuroimaging techniques has generated large-scale multimodal neuroimaging data. A systematic review of publications using deep learning approaches and neuroimaging data for diagnostic classification of AD was performed. A PubMed and Google Scholar search was used to identify deep learning papers on…

• UD
  U.S. Department of Defense

  Awards: U01 AG024904, W81XWH, W81XWH-12-2-, W81XWH-12-2-0012, AG024904
• AD
  Alzheimer's Disease Neuroimaging Initiative

  Awards: AG024904, W81XWH-12-2-, W81XWH-12-2-0012, U01 AG024904
• NI
  National Institutes of Health

  Awards: R01 AG19771, AG024904, R01 LM012535, U01 AG024904, W81XWH, R03 AG054936, W81XWH-12-2-0012, R01 AG057739, CA129769, R01 CA129769, P30 AG10133
• NI
  National Institute on Aging

  Award: R03 AG054936
• UN
  U.S. National Library of Medicine

  Award: R01 LM012535