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P1‐349: ADVANCING CLINICAL AND BIOMARKER RESEARCH IN AD: THE LEAD STUDY
Author(s) -
Apostolova Liana G.,
Iaccarino Leonardo,
Collins Jessica A.,
Aisen Paul S.,
Borowski Bret J.,
Eloyan Ani,
Fagan Anne M.,
Foroud Tatiana M.,
Gatsonis Constantine,
Jack Clifford R.,
Kramer Joel H.,
Koeppe Robert A.,
Saykin Andrew J.,
Toga Arthur W.,
Vemuri Prashanthi,
Day Gregory S.,
Graff-Radford Neill R.,
Honig Lawrence S.,
Jones David T.,
Masdeu Joseph C.,
Mendez Mario F.,
Onyike Chiadi U.,
Rogalski Emily J.,
Salloway Stephen,
Wolk David A.,
Wingo Thomas S.,
Rabinovici Gil D.,
Dickerson Brad C.,
Carrillo Maria C.
Publication year - 2019
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 - 10.1016/j.jalz.2019.06.904
Subject(s) - early onset alzheimer's disease , biomarker , medicine , dementia , neuroimaging , entorhinal cortex , oncology , disease , psychology , hippocampal formation , psychiatry , genetics , biology
discrimination. Visualization of learning process by guided GradCAM revealed that CIS became more focused by the CNN as the training progressed. DLB/AD score was significantly associated with three core-features of DLB. Conclusions: Deep learning-based imaging classification was useful not only for objective and accurate differentiation of DLB from AD but also for predicting clinical features of DLB. The CIS was identified as a specific feature during DLB classification. The visualization of specific features and learning process could have important implications for the potential of deep learning to discover new imaging features.