{"id":"W3130700206","doi":"10.3389/fpsyt.2021.598518","title":"Predicting Brain Age at Slice Level: Convolutional Neural Networks and Consequences for Interpretability","year":2021,"lang":"en","type":"article","venue":"Frontiers in Psychiatry","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Joseph’s Healthcare Hamilton; McMaster University","funders":"","keywords":"Interpretability; Neuroimaging; Brain aging; Dementia; White matter; Psychology; Convolutional neural network; Aging brain; Magnetic resonance imaging; Cognition; Neuroscience; Cognitive psychology; Medicine; Disease; Artificial intelligence; Computer science; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006453544,0.0001573435,0.0002466822,0.00007717682,0.0002271221,0.0000913995,0.0004196677,0.0001278292,0.000008472817],"category_scores_gemma":[0.0004190436,0.0001682556,0.00007154418,0.0002934439,0.0001977609,0.0002235545,0.0002964074,0.0003846989,4.601923e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001243609,"about_ca_system_score_gemma":0.0002111669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009761379,"about_ca_topic_score_gemma":0.0005524576,"domain_scores_codex":[0.9980979,0.0003281302,0.0003704256,0.000638273,0.0001769837,0.0003882614],"domain_scores_gemma":[0.9989452,0.0003434661,0.0001245491,0.0003936203,0.0000757508,0.0001174451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003065944,0.00002279631,0.9636216,0.0001097087,0.00001495945,0.000009070217,0.0004415211,0.002261209,0.000005225029,0.003595886,0.02475476,0.005132652],"study_design_scores_gemma":[0.0004737204,0.0000611618,0.1833819,0.00006596199,0.000004586343,0.00006322846,0.0002051767,0.8062339,0.000003438491,0.00812679,0.001208862,0.0001713038],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1866491,0.005398223,0.7697647,0.02071707,0.01669805,0.0003498773,0.00002764918,0.0001346752,0.0002607005],"genre_scores_gemma":[0.749663,0.00001392426,0.2475735,0.002277327,0.0002300395,0.00004096356,0.00002063955,0.00001221795,0.0001683538],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8039727,"threshold_uncertainty_score":0.6861266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01838638151613122,"score_gpt":0.2836341978661762,"score_spread":0.265247816350045,"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."}}