{"id":"W2995372948","doi":"10.1101/2019.12.20.884932","title":"Transfer Learning for Predicting Conversion from Mild Cognitive Impairment to Dementia of Alzheimer’s Type based on 3D-Convolutional Neural Network","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Institute on Aging; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Eli Lilly and Company; U.S. Department of Defense; Compute Canada; Northern California Institute for Research and Education; Fondation Brain Canada; Natural Sciences and Engineering Research Council of Canada; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Dementia; Cognition; Convolutional neural network; Artificial intelligence; Deep learning; Cognitive decline; Alzheimer's disease; Psychology; Computer science; Disease; Medicine; Neuroscience; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001030575,0.001149101,0.0006126305,0.0009416196,0.0002652589,0.000610565,0.0008215596,0.001195824,0.001043884],"category_scores_gemma":[0.00176254,0.0003784732,0.0008969269,0.0004606114,0.0003814531,0.0004510631,0.000649666,0.001165775,0.0002563934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00137056,"about_ca_system_score_gemma":0.0008591416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02472133,"about_ca_topic_score_gemma":0.01639446,"domain_scores_codex":[0.9997749,0.00004829231,0.00001589246,0.00006750326,0.00003703625,0.00005646427],"domain_scores_gemma":[0.9994116,0.0002975499,0.00006647877,0.00003820895,0.0001390477,0.00004715162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006994325,0.0005020065,0.03701969,0.00007310775,0.0002557602,0.0003980969,0.00005828335,0.853904,0.004894176,0.0005763403,0.002839526,0.09877954],"study_design_scores_gemma":[0.000003267107,0.00002149341,0.001033652,0.000003405869,0.0000071502,0.00001087895,0.000003593886,0.9982291,0.0004322801,0.0002184401,0.00003358787,0.000003144365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8654774,0.001491455,0.1277093,0.0007893863,0.0001803194,0.0001243547,0.001014138,0.001492882,0.001720668],"genre_scores_gemma":[0.9896408,0.000135981,0.008555242,0.00009435169,0.00001625191,0.00004014324,0.0006454261,0.00001597209,0.0008558335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02472133,"threshold_uncertainty_score":0.04915488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02654861974424242,"score_gpt":0.2722250316175075,"score_spread":0.2456764118732651,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}