{"id":"W3123500501","doi":"10.3390/s21030778","title":"Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":65,"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; 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":"Dementia; Neuroimaging; Computer science; Pipeline (software); Cognition; Convolutional neural network; Artificial intelligence; Disease; Alzheimer's disease; Machine learning; Cognitive impairment; Medicine; Neuroscience; Psychology; Pathology","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":[0.0002348718,0.00006032077,0.0001183634,0.0000914359,0.00006623386,0.00001458968,0.00001528213,0.00004141893,0.00007837322],"category_scores_gemma":[0.0004947783,0.00005793362,0.00005187208,0.0001649245,0.00003556344,0.00003361106,0.00001940095,0.00009845077,0.000004427169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001466181,"about_ca_system_score_gemma":0.00002492562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003318757,"about_ca_topic_score_gemma":0.00002404415,"domain_scores_codex":[0.9993208,0.00007494479,0.0001373173,0.000168865,0.0001650326,0.0001330285],"domain_scores_gemma":[0.9994381,0.0001809598,0.00004951018,0.00007501937,0.0001977838,0.00005864055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001358354,0.0001005729,0.8221505,0.000132842,0.0001297985,0.000006525909,0.0001036034,0.000001169421,0.07469494,0.00005991744,0.00004680466,0.1024375],"study_design_scores_gemma":[0.001141138,0.0005436772,0.7608165,0.0000396999,0.0001298117,0.00001105992,0.0003882827,0.002078868,0.2311274,0.00005070674,0.00362033,0.00005252131],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962273,0.0003572322,0.00124804,0.00102187,0.00003368642,0.0002666112,0.000006626869,0.00001836729,0.0008202572],"genre_scores_gemma":[0.9979724,0.0001620005,0.0004029407,0.00007811232,0.00003581962,0.00004151766,0.00003263655,0.00001144051,0.001263181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1564325,"threshold_uncertainty_score":0.2362464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03181282074798456,"score_gpt":0.3121969652598167,"score_spread":0.2803841445118321,"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."}}