{"id":"W3173571517","doi":"10.30630/joiv.5.2.572","title":"3D CNN based Alzheimer’s diseases classification using segmented Grey matter extracted from whole-brain MRI","year":2021,"lang":"en","type":"article","venue":"JOIV International Journal on Informatics Visualization","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":9,"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; Meso Scale Diagnostics; National Research Foundation of Korea; Chosun University; National Research Foundation; Northern California Institute for Research and Education; Novartis Pharmaceuticals Corporation; Ministry of Science and ICT, South Korea; Biogen; BioClinica; University of Southern California; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Grey matter; Magnetic resonance imaging; Dementia; Voxel; Voxel-based morphometry; Convolutional neural network; Neuroimaging; Artificial intelligence; Positron emission tomography; Nuclear medicine; Feature (linguistics); Medicine; Pattern recognition (psychology); Computer science; Radiology; Psychology; Neuroscience; Pathology; White matter; Disease","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":[0.0003905234,0.0009655714,0.0005691954,0.001435771,0.0002082805,0.0008113076,0.0006161868,0.0007799817,0.002187538],"category_scores_gemma":[0.0006531112,0.0003313567,0.001196847,0.0006163527,0.0001994418,0.0003833101,0.0005099353,0.0003847615,0.00116181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007479528,"about_ca_system_score_gemma":0.0005355637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01716892,"about_ca_topic_score_gemma":0.01327574,"domain_scores_codex":[0.9997664,0.00001315524,0.00001849609,0.00007649903,0.00006334812,0.00006207595],"domain_scores_gemma":[0.9998242,0.0000290169,0.00002893687,0.00002576783,0.00007351003,0.00001847933],"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":[0.001490782,0.000279298,0.03901869,0.0003354,0.0005589827,0.001617215,0.000182041,0.1152935,0.1036381,0.001002252,0.01016691,0.7264168],"study_design_scores_gemma":[0.00002480941,0.0002065499,0.02751381,0.00005863101,0.0001673327,0.0009774291,0.00006070958,0.9298835,0.03654187,0.001139062,0.003382397,0.00004401922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6610053,0.003365804,0.3071135,0.0006885875,0.0006480227,0.0004738025,0.007557524,0.009796742,0.009350649],"genre_scores_gemma":[0.8916492,0.001356762,0.09067899,0.0002116528,0.00009958083,0.0001536035,0.007625911,0.0001416479,0.008082731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01716892,"threshold_uncertainty_score":0.03413796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05761418459364067,"score_gpt":0.3382822081045394,"score_spread":0.2806680235108988,"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."}}