{"id":"W3006901343","doi":"10.1016/j.imu.2020.100305","title":"Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer's disease using structural MRI analysis","year":2020,"lang":"en","type":"article","venue":"Informatics in Medicine Unlocked","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Johnson and Johnson Pharmaceutical Research and Development; National Institute of Biomedical Imaging and Bioengineering; Fujirebio Europe; National Institute on Aging; Genentech; National Institutes of Health; DoD Alzheimer's Disease Neuroimaging Initiative; H. Lundbeck A/S; Alzheimer's Disease Neuroimaging Initiative; U.S. Department of Defense; GE Healthcare; Janssen Alzheimer Immunotherapy Research And Development; Northern California Institute for Research and Education; Canadian Institutes of Health Research; University of Southern California","keywords":"Dementia; Cognition; Neuroimaging; Magnetic resonance imaging; Alzheimer's disease; Disease; Cognitive impairment; Receiver operating characteristic; Psychology; Cohen's kappa; Audiology; Medicine; Pathology; Neuroscience; Internal medicine; Radiology; Machine learning; Computer science","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":[0.001252736,0.000450081,0.0005390024,0.002419999,0.0002192551,0.0005788004,0.0003970819,0.0004333268,0.000341147],"category_scores_gemma":[0.003965723,0.0001525334,0.0003790353,0.0006131083,0.0002215788,0.0004693486,0.0003390328,0.0002160858,0.000179871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002370641,"about_ca_system_score_gemma":0.0003691345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003444724,"about_ca_topic_score_gemma":0.006251977,"domain_scores_codex":[0.999447,0.0001770215,0.0000689328,0.0001235988,0.000125865,0.00005748839],"domain_scores_gemma":[0.9986695,0.0005385539,0.0002671749,0.0001414734,0.0003172875,0.00006603874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009550164,0.0003947513,0.6888869,0.0001407777,0.0004242577,0.0005796526,0.0002350742,0.01346035,0.04759488,0.0005100646,0.00173965,0.2450785],"study_design_scores_gemma":[0.00004952565,0.0003053171,0.7682413,0.00002463992,0.0001266776,0.001430429,0.0002072472,0.2153887,0.01160522,0.00169578,0.0008672362,0.00005792936],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9772035,0.0004821311,0.02095729,0.00005659266,0.00001520628,0.00004097442,0.0004097567,0.0002942701,0.0005402476],"genre_scores_gemma":[0.9807782,0.000135264,0.01802682,0.00003196625,0.00002068369,0.00002690299,0.0007400163,0.00001083745,0.0002292601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003444724,"threshold_uncertainty_score":0.006849349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05855819428330351,"score_gpt":0.3624484488159614,"score_spread":0.3038902545326579,"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."}}