{"id":"W2764239667","doi":"10.4236/jamp.2017.59159","title":"Automatic Alzheimer’s Disease Recognition from MRI Data Using Deep Learning Method","year":2017,"lang":"en","type":"article","venue":"Journal of Applied Mathematics and Physics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; University of Southern California; Biogen; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Convolutional neural network; Deep learning; Computer science; Neuroimaging; Dementia; Pattern recognition (psychology); Pooling; Neuropsychology; Normalization (sociology); Machine learning; Disease; Medicine; Cognition; Pathology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000621338,0.0007316693,0.0006404772,0.001480326,0.0002432709,0.0004722256,0.0005304461,0.0006346796,0.0008322515],"category_scores_gemma":[0.00133877,0.000269941,0.0005183595,0.0008290534,0.0001782604,0.0005779479,0.0004418271,0.0006201664,0.0004239308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004140656,"about_ca_system_score_gemma":0.0006542357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00481493,"about_ca_topic_score_gemma":0.005535527,"domain_scores_codex":[0.9996996,0.00005227308,0.00003809747,0.00008899366,0.00006714999,0.00005396699],"domain_scores_gemma":[0.9996081,0.0001293335,0.00005088509,0.00005495126,0.0001366332,0.00002003686],"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":[0.0005892407,0.0004065955,0.01140393,0.0002075922,0.0001936368,0.0004197377,0.00009354219,0.0767684,0.04660386,0.001208664,0.006123162,0.8559817],"study_design_scores_gemma":[0.00002372775,0.00007993839,0.005508846,0.00001813235,0.00003721318,0.0002382767,0.00002268903,0.9759833,0.01541484,0.001670551,0.0009823737,0.00002006708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.301623,0.002423652,0.6868257,0.0004330393,0.0001855678,0.000213335,0.001464774,0.00465819,0.002172761],"genre_scores_gemma":[0.7949635,0.000749028,0.199582,0.0001512052,0.00008715683,0.0001476448,0.002502586,0.00005398329,0.001762862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00481493,"threshold_uncertainty_score":0.009573817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.187171417727773,"score_gpt":0.3571560132734274,"score_spread":0.1699845955456545,"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."}}