{"id":"W4403269332","doi":"10.3389/fnagi.2024.1434589","title":"Machine learning models for diagnosing Alzheimer’s disease using brain cortical complexity","year":2024,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alzheimer's Disease Neuroimaging Initiative","keywords":"Receiver operating characteristic; Montreal Cognitive Assessment; Area under the curve; Apolipoprotein E; Cognition; Internal medicine; Medicine; Psychology; Artificial intelligence; Disease; Cognitive impairment; Computer science; Neuroscience","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003192407,0.001172449,0.0007319772,0.001959508,0.0002953542,0.001240297,0.0007447248,0.0006777447,0.000626304],"category_scores_gemma":[0.008829748,0.0002406778,0.0009492934,0.0007357355,0.0003769378,0.000781225,0.0006141298,0.0007893817,0.0003120917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006147174,"about_ca_system_score_gemma":0.0006369122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003870802,"about_ca_topic_score_gemma":0.002666453,"domain_scores_codex":[0.9991528,0.0003469194,0.00006344995,0.0002218969,0.0001404765,0.00007453479],"domain_scores_gemma":[0.9963329,0.002621227,0.000401601,0.0001731732,0.0003992376,0.00007192547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004494163,0.0003335744,0.1277935,0.0001319062,0.0005701666,0.0002111996,0.0001089233,0.7401639,0.002921531,0.001456644,0.001745323,0.1241139],"study_design_scores_gemma":[0.00001422398,0.00009777377,0.00623265,0.00002469514,0.00003765947,0.00007251385,0.00002138362,0.9908966,0.0004761459,0.001815701,0.0002980369,0.00001246761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.561503,0.003071294,0.4288925,0.000983173,0.0001505155,0.0002327415,0.001209494,0.00127914,0.002678143],"genre_scores_gemma":[0.9518781,0.0003530904,0.0462513,0.0001213323,0.00006928871,0.0001239864,0.0005908437,0.000022294,0.0005896942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003870802,"threshold_uncertainty_score":0.01688325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1227725336711663,"score_gpt":0.376714446357714,"score_spread":0.2539419126865476,"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."}}