{"id":"W2905035821","doi":"10.1016/j.nicl.2018.101645","title":"Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks","year":2018,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":720,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Research Council; National Institutes of Health; Agenzia di Ricerca per la Sclerosi Laterale Amiotrofica; Ministry of Health, British Columbia; Alzheimer's Disease Neuroimaging Initiative; National Institute on Aging; Eli Lilly and Company; National Institute of Biomedical Imaging and Bioengineering; F. Hoffmann-La Roche; U.S. Department of Defense","keywords":"Cognitive impairment; Neuroimaging; Convolutional neural network; Artificial intelligence; Alzheimer's Disease Neuroimaging Initiative; Deep learning; Cognition; Mri scan; Magnetic resonance imaging; Computer science; Psychology; Pattern recognition (psychology); Medicine; Neuroscience; Radiology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001291722,0.0007167066,0.0005627038,0.001244873,0.0002096481,0.0006263571,0.0005588878,0.000603198,0.0006302894],"category_scores_gemma":[0.003422834,0.0002711316,0.0004167661,0.00050007,0.0002905598,0.000506404,0.0008118067,0.0004048607,0.0002903773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006605982,"about_ca_system_score_gemma":0.0007695356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009534633,"about_ca_topic_score_gemma":0.01897617,"domain_scores_codex":[0.9994766,0.00009998825,0.00004992621,0.0001939545,0.00009673354,0.00008278241],"domain_scores_gemma":[0.9991845,0.000264171,0.0001653641,0.0001405273,0.0001643924,0.00008107747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00252834,0.0007960252,0.4020143,0.0002077963,0.000615145,0.0009508263,0.0003074972,0.05749058,0.04696852,0.001170167,0.00347933,0.4834715],"study_design_scores_gemma":[0.0001257712,0.0006073375,0.2966086,0.00008017654,0.0002674662,0.001288102,0.000161117,0.6664738,0.02852746,0.003952486,0.001817925,0.00008977933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9728369,0.0005362885,0.02374287,0.0001876902,0.00004203428,0.00008629761,0.001048645,0.0003817082,0.001137631],"genre_scores_gemma":[0.9799801,0.000161613,0.01805581,0.00008589719,0.00003352381,0.00003907001,0.00107811,0.00001265293,0.0005532648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009534633,"threshold_uncertainty_score":0.01895827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1105954610124805,"score_gpt":0.420937875063728,"score_spread":0.3103424140512475,"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."}}