{"id":"W4407348615","doi":"10.1002/brb3.70299","title":"A Neural Network Approach to Identify Left–Right Orientation of Anatomical Brain MRI","year":2025,"lang":"en","type":"article","venue":"Brain and Behavior","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Research Resources; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; National Institute of Mental Health; Novartis Pharmaceuticals Corporation; Pfizer; National Institute on Aging; Alzheimer's Association","keywords":"Orientation (vector space); Convolutional neural network; Artificial intelligence; Planum temporale; Computer science; Psychology; Medicine; Neuroscience","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":[0.0002671691,0.0001403378,0.0002105223,0.000140502,0.0002076477,0.00005088153,0.0001484072,0.00006382628,0.00002144249],"category_scores_gemma":[0.001738143,0.0001293654,0.00006398676,0.0005028283,0.0001564498,0.0001336165,0.0001695795,0.0001286732,0.000006428048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003284543,"about_ca_system_score_gemma":0.00003125668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002148741,"about_ca_topic_score_gemma":0.0000235342,"domain_scores_codex":[0.998635,0.0001506625,0.0002293481,0.0005190645,0.0002275755,0.0002383348],"domain_scores_gemma":[0.9978766,0.001762471,0.00005510414,0.0001938989,0.00004339506,0.00006852714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003604786,0.001414659,0.09230281,0.0001896699,0.00003606732,0.00003744705,0.001689972,0.000544968,0.4878168,0.0583558,0.3404882,0.01676317],"study_design_scores_gemma":[0.001436357,0.0002367848,0.9291711,0.00005854348,0.000102602,0.00005121078,0.0003231493,0.001649598,0.04808684,0.001651615,0.01680755,0.0004245917],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853604,0.00004432604,0.001288836,0.01064629,0.0005715495,0.0005161466,0.00002061853,0.00006150195,0.001490338],"genre_scores_gemma":[0.9897416,0.000002934375,0.0005109656,0.007763327,0.00007965087,0.00007255338,0.000005003652,0.00001061576,0.001813318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8368683,"threshold_uncertainty_score":0.5275368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02986908391446162,"score_gpt":0.3235856543282374,"score_spread":0.2937165704137757,"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."}}