{"id":"W2790040701","doi":"10.1155/2018/1247430","title":"Classification of Alzheimer’s and MCI Patients from Semantically Parcelled PET Images: A Comparison between AV45 and FDG-PET","year":2018,"lang":"en","type":"article","venue":"International Journal of Biomedical Imaging","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","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; BioClinica; F. Hoffmann-La Roche; University of Southern California; Biogen; 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":"Positron emission tomography; Dementia; Cognitive impairment; Neuroimaging; Random forest; Computer science; Artificial intelligence; Pet imaging; Cognition; Medicine; Nuclear medicine; Disease; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004749991,0.0001067291,0.0003178199,0.0003109738,0.00003911262,0.00005389922,0.0001471784,0.0000230503,0.0002119941],"category_scores_gemma":[0.0003487758,0.00008245445,0.00006606415,0.00008858437,0.0006545988,0.0001781506,0.0001196877,0.0002209316,0.000007480165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003081417,"about_ca_system_score_gemma":0.00008427512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004103009,"about_ca_topic_score_gemma":9.200181e-7,"domain_scores_codex":[0.9978803,0.00007659334,0.0006553501,0.000166236,0.00106331,0.0001582316],"domain_scores_gemma":[0.9980319,0.0001944624,0.0003398003,0.00007149816,0.001105876,0.0002564394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002771359,0.0003867901,0.8849677,0.00001565392,0.0006119821,0.0001015784,0.0002118015,5.252005e-9,0.0208925,0.00001339928,0.001619557,0.09090195],"study_design_scores_gemma":[0.003692277,0.0005026549,0.9870476,0.0003701321,0.0003226614,0.0001216653,0.0003732686,0.001293379,0.004735582,0.0003634518,0.00110221,0.00007518933],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889782,0.000256065,0.004452074,0.005499484,0.000204439,0.0001177464,0.0000406927,0.000006291125,0.0004449937],"genre_scores_gemma":[0.9966539,0.00007836019,0.002483034,0.000214834,0.0004672127,0.000001410974,0.00007255092,0.000009653044,0.0000191129],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1020799,"threshold_uncertainty_score":0.3362395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0296735594575597,"score_gpt":0.3699839515686719,"score_spread":0.3403103921111121,"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."}}