{"id":"W2916257687","doi":"10.3389/fnins.2019.00509","title":"Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network","year":2019,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":294,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Science and Technology Commission of Shanghai Municipality; Novartis Pharmaceuticals Corporation; Pfizer; National Institute on Aging; Alzheimer's Association","keywords":"Convolutional neural network; Artificial intelligence; Cognitive impairment; Modality (human–computer interaction); Computer science; Classifier (UML); Pattern recognition (psychology); Neuroimaging; Segmentation; Cognition; Task (project management); Deep learning; Neuroscience; Psychology","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":[0.0003575136,0.0001194219,0.0002422515,0.0001319709,0.0000572179,0.00001449084,0.0001957626,0.00003175563,0.00009785296],"category_scores_gemma":[0.0001684912,0.0001084857,0.00008509513,0.000529676,0.0003897959,0.0001781249,0.0001223107,0.0001850861,0.00001193179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003703485,"about_ca_system_score_gemma":0.0001439731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002437007,"about_ca_topic_score_gemma":0.000001350236,"domain_scores_codex":[0.9981722,0.00011193,0.0002532501,0.0004308654,0.0005906327,0.0004411456],"domain_scores_gemma":[0.9993057,0.00003951182,0.0000729969,0.0002591706,0.00008671573,0.0002359052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001655811,0.0003699208,0.9930052,0.0000371924,0.000006040865,0.00003725687,0.00001562689,0.0004144037,0.0008767336,0.00002321995,0.001709291,0.003339543],"study_design_scores_gemma":[0.001046343,0.0001741093,0.9008276,0.00004519209,0.00003378921,0.000003256622,0.00001308278,0.09662338,0.0003414437,0.00009793865,0.0007060831,0.00008782027],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887543,0.0009182529,0.006355199,0.0009613747,0.001732898,0.0009066303,0.00002272547,0.00002526849,0.000323392],"genre_scores_gemma":[0.9970395,0.00009287195,0.001645351,0.0007900719,0.00004391894,0.00005813314,0.000008218454,0.000009300539,0.0003126359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09620897,"threshold_uncertainty_score":0.4423919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03012359091555377,"score_gpt":0.3139453316737126,"score_spread":0.2838217407581589,"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."}}