{"id":"W2980845730","doi":"10.1016/j.jalz.2019.08.136","title":"P4‐588: END‐TO‐END 3D‐CONVOLUTIONAL NEURAL NETWORK FOR PREDICTING CONVERSION FROM MILD COGNITIVE IMPAIRMENT TO ALZHEIMER'S DEMENTIA","year":2019,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Dementia; Cognitive impairment; Convolutional neural network; Neuroimaging; Artificial intelligence; Cognition; Psychology; Audiology; Pattern recognition (psychology); Computer science; Neuroscience; Medicine; Disease; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008651328,0.00155838,0.0004319934,0.0006704719,0.0003288864,0.00053392,0.001169738,0.0009584091,0.007759435],"category_scores_gemma":[0.00156619,0.0003783985,0.0008853307,0.0003222841,0.0002310097,0.0006235763,0.0007770472,0.001045288,0.003111208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007912193,"about_ca_system_score_gemma":0.001243972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01901033,"about_ca_topic_score_gemma":0.02642537,"domain_scores_codex":[0.9997628,0.00003111235,0.000008575117,0.00008840031,0.00005567251,0.0000533994],"domain_scores_gemma":[0.9997489,0.00007036331,0.0000183181,0.00003625366,0.0001002536,0.0000258545],"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":[0.002456209,0.0009746023,0.04598945,0.0002157805,0.0005907015,0.0005963536,0.0000824001,0.1992434,0.02570292,0.002359763,0.06462991,0.6571584],"study_design_scores_gemma":[0.00008610019,0.0003026221,0.01057072,0.00003513748,0.00009035148,0.0001851959,0.00002131483,0.9646684,0.01700353,0.002024274,0.004980479,0.00003181836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.618267,0.002173362,0.3134616,0.001243083,0.0005431647,0.001034273,0.02614052,0.01533103,0.02180588],"genre_scores_gemma":[0.8109193,0.0005989693,0.1341486,0.0004098734,0.00007836788,0.0007087903,0.02913193,0.0004501638,0.02355392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01901033,"threshold_uncertainty_score":0.03779936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04145980898821827,"score_gpt":0.2741973757169501,"score_spread":0.2327375667287318,"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."}}