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Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review.

Robin Jacob BorchertTiago AzevedoAmanPreet BadhwarJose BernalMatthew BettsRose BruffaertsMichael C BurkhartIlse DewachterHelena M GellersenAudrey LowIlianna LouridaLuiza Santos MachadoChristopher R MadanMaura MalpettiJhony MejiaSofia MichopoulouCarlos Muñoz-NeiraJack PepysMarion PeresVeronica PhillipsSiddharth RamananStefano TamburinHanz M TantiangcoLokendra ThakurAlessandro TomassiniAshwati VipinEugene Yee Hing TangDanielle Newbynull nullJanice M RansonDavid J LlewellynMichele VeldsmanTimothy Rittman
Published in: Alzheimer's & dementia : the journal of the Alzheimer's Association (2023)
There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.
Keyphrases
  • artificial intelligence
  • machine learning
  • deep learning
  • neural network
  • big data
  • mild cognitive impairment
  • drinking water
  • cognitive decline
  • cognitive impairment
  • quantum dots