{"id":"W4405987221","doi":"10.1016/j.artmed.2024.103063","title":"A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"NIH Blueprint for Neuroscience Research; National Institute of Mental Health; McDonnell Center for Systems Neuroscience; Pittsburgh Foundation; Ministero della Salute; Ministero dell’Istruzione, dell’Università e della Ricerca; National Institutes of Health; Fondazione Cariplo; European Commission","keywords":"Feature (linguistics); Normative; Computer science; Artificial intelligence; Bipolar disorder; Anomaly detection; Pattern recognition (psychology); Encoder; Anomaly (physics); Psychology; Neuroscience; Cognition; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00220413,0.001179882,0.0006510693,0.0007150859,0.0004471072,0.0006709203,0.001109376,0.001101501,0.001146335],"category_scores_gemma":[0.004711128,0.0003437331,0.0009781278,0.0004037203,0.0005702059,0.0004689083,0.001387566,0.001525167,0.0006732729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006444028,"about_ca_system_score_gemma":0.0009376759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01283658,"about_ca_topic_score_gemma":0.01858048,"domain_scores_codex":[0.9993566,0.0001932758,0.00004798816,0.0002238278,0.0001021504,0.00007618662],"domain_scores_gemma":[0.9990048,0.0003208672,0.00007378319,0.0002552481,0.000288425,0.00005688672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00131194,0.0006463209,0.05135687,0.0003216284,0.0008130558,0.001287454,0.0004788436,0.4164169,0.04484542,0.003020657,0.0324348,0.4470661],"study_design_scores_gemma":[0.00005401327,0.0001018104,0.01030865,0.00003475042,0.00004793317,0.0002485072,0.00005615779,0.9776354,0.006871777,0.002167625,0.00244155,0.00003182003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6268645,0.001376874,0.3471497,0.001045951,0.0003134639,0.0002751273,0.007773414,0.012624,0.002576993],"genre_scores_gemma":[0.8412577,0.0002382278,0.1305801,0.000357446,0.0000623516,0.000266158,0.0237953,0.0005189908,0.002923689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01283658,"threshold_uncertainty_score":0.02552372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08969466480174905,"score_gpt":0.3869742770569827,"score_spread":0.2972796122552337,"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."}}