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Predicting Alzheimer's disease progression: Results from the TADPOLE Challenge
Author(s) -
Marinescu Razvan V.,
Bron Esther E.,
Oxtoby Neil P.,
Young Alexandra L.,
Toga Arthur W.,
Weiner Michael W.,
Barkhof Frederik,
Fox Nick C.,
Golland Polina,
Klein Stefan,
Alexander Daniel C.
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.039538
Subject(s) - disease , diffusion mri , medicine , clinical trial , physical medicine and rehabilitation , machine learning , artificial intelligence , computer science , radiology , magnetic resonance imaging
Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials, at the right time. However, an unbiased comparison of state‐of‐the‐art algorithms for predicting disease onset and progression is currently lacking.