{"id":"W4410074680","doi":"10.63471/am24003","title":"Deep Learning Models for Early Detection of Alzheimer’s Disease Using Neuroimaging Data","year":2024,"lang":"en","type":"article","venue":"Journal of Advances in Medical Sciences and Artificial Intelligence","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wycliffe College","funders":"","keywords":"Neuroimaging; Alzheimer's Disease Neuroimaging Initiative; Deep learning; Artificial intelligence; Neuroscience; Disease; Alzheimer's disease; Computer science; Psychology; Machine learning; Data science; Medicine; Internal medicine","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":[0.001539826,0.00007645317,0.0001397319,0.0002076968,0.000175229,0.0001152376,0.0003881484,0.00002861424,0.00001426754],"category_scores_gemma":[0.001891983,0.00005913589,0.00004348802,0.000596948,0.0005766234,0.001534531,0.00006820058,0.0002727622,5.448293e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001511248,"about_ca_system_score_gemma":0.0001089222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008799399,"about_ca_topic_score_gemma":0.00001470133,"domain_scores_codex":[0.998298,0.0001067593,0.0005543073,0.0003070199,0.0005642448,0.0001696669],"domain_scores_gemma":[0.998961,0.0005508583,0.0002166362,0.00008732701,0.00004226954,0.0001419126],"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":[0.0000576673,0.00003794739,0.00008198225,0.00004034156,0.000001703276,0.00002679585,0.0002131899,0.0347378,0.02787195,0.01000892,3.830292e-7,0.9269213],"study_design_scores_gemma":[0.00002027031,0.0001435216,0.00005656194,0.0001763041,0.00001404389,0.00005022461,0.0003050504,0.9349283,0.0257989,0.03816124,0.0002831234,0.00006246666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1994183,0.005748212,0.7929779,0.0007037711,0.0009746521,0.0001071086,0.000002384546,0.00001378061,0.00005386995],"genre_scores_gemma":[0.9969682,0.001810922,0.001001612,0.00005762656,0.000153014,0.000001498416,1.326535e-7,0.000005218204,0.000001817358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9268588,"threshold_uncertainty_score":0.2411492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2424978383686097,"score_gpt":0.4155962429471712,"score_spread":0.1730984045785616,"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."}}