{"id":"W4285246255","doi":"10.1109/access.2022.3180073","title":"Automated Detection of Alzheimer’s Disease and Mild Cognitive Impairment Using Whole Brain MRI","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"DoD Alzheimer's Disease Neuroimaging Initiative; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Eisai; National Research Foundation of Korea; National Research Foundation; Ministry of Education; Ministry of Science and ICT, South Korea; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Northern California Institute for Research and Education; F. Hoffmann-La Roche; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Cognitive impairment; Disease; Computer science; Cognition; Medicine; Artificial intelligence; Neuroscience; Psychology; Internal medicine","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":[0.0001619001,0.0000911055,0.00009677676,0.0001381917,0.0003702182,0.00005954592,0.000135002,0.0000183296,0.00005484374],"category_scores_gemma":[0.00008344369,0.00009861111,0.00003761087,0.0004500938,0.00009120239,0.0002729878,0.0000922508,0.0001144438,0.000003634227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005107317,"about_ca_system_score_gemma":0.00004461023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000495442,"about_ca_topic_score_gemma":0.000005285797,"domain_scores_codex":[0.9988757,0.0002357302,0.0001854092,0.0003109328,0.000253899,0.0001383606],"domain_scores_gemma":[0.9994716,0.0001087263,0.0001696843,0.0001301747,0.00003287784,0.00008691749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002086129,0.0001581084,0.000468789,0.00001978703,0.00001163523,0.00001064281,0.000221154,0.0008368557,0.9949492,0.00001792328,0.0003379038,0.002759421],"study_design_scores_gemma":[0.0007156006,0.0001111209,0.04128856,0.00001596347,0.0000609358,0.00002501713,0.0002109087,0.1569312,0.8000841,0.0001332554,0.0002718265,0.0001515533],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970143,0.00005447257,0.001280625,0.0005199351,0.0003988169,0.0003970857,0.00007627885,0.0002021485,0.00005629129],"genre_scores_gemma":[0.9990935,0.000003588863,0.000007374651,0.0007279748,0.00002858786,0.00007806483,0.000003199126,0.00001446446,0.00004323245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1948651,"threshold_uncertainty_score":0.4021244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07835892160765011,"score_gpt":0.3447937614522636,"score_spread":0.2664348398446135,"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."}}