{"id":"W3110796009","doi":"10.1038/s41598-020-79243-9","title":"Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":164,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Institute for Information and Communications Technology Promotion; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; Servier; Eisai; Pfizer; Novartis Pharmaceuticals Corporation; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Ministry of Science and ICT, South Korea; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Bristol-Myers Squibb; Eli Lilly and Company; Biogen","keywords":"Generalizability theory; Convolutional neural network; Neuroimaging; Magnetic resonance imaging; Population; Artificial intelligence; Computer science; Cross-validation; Temporal lobe; Pattern recognition (psychology); Machine learning; Medicine; Psychology; Radiology; Psychiatry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008614596,0.0001436749,0.0002077615,0.000147628,0.0002373818,0.00009797578,0.00008445777,0.00002513886,0.000202931],"category_scores_gemma":[0.0002030738,0.0001341851,0.0001394547,0.0007229928,0.0003430063,0.0001385777,0.00005703411,0.000135713,0.00001135342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004328145,"about_ca_system_score_gemma":0.000570251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005125645,"about_ca_topic_score_gemma":3.138585e-7,"domain_scores_codex":[0.9970611,0.00007831566,0.0006097287,0.0007254454,0.001159229,0.000366213],"domain_scores_gemma":[0.998411,0.00002774161,0.0002557343,0.0004679722,0.0004762429,0.0003613202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00159025,0.001064563,0.6878566,0.0002896409,0.00006047406,0.002504764,0.0002435051,0.07115196,0.1966123,0.0002390338,0.01803068,0.02035616],"study_design_scores_gemma":[0.0004517704,0.00006432945,0.08465403,0.0001060125,0.0001635288,0.000020926,0.00001295501,0.9079928,0.005325525,0.0007523169,0.0003465301,0.0001093002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834824,0.003054914,0.008869509,0.001839841,0.0009994187,0.001111702,0.00002161522,0.00008087817,0.0005397809],"genre_scores_gemma":[0.9983738,0.000001970229,0.0006629,0.0004171148,0.0001057523,0.0000267041,0.0001155467,0.00001854234,0.000277648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8368408,"threshold_uncertainty_score":0.5471911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03180385984200299,"score_gpt":0.3051262475838256,"score_spread":0.2733223877418225,"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."}}