{"id":"W4405219981","doi":"10.1177/25424823241290694","title":"MRI-based mild cognitive impairment and Alzheimer's disease classification using an algorithm of combination of variational autoencoder and other machine learning classifiers","year":2024,"lang":"en","type":"article","venue":"Journal of Alzheimer s Disease Reports","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Autoencoder; Artificial intelligence; Support vector machine; Machine learning; Dementia; Feature selection; Magnetic resonance imaging; Pattern recognition (psychology); Cognitive impairment; Classifier (UML); Cross-validation; Neuroimaging; Statistical classification; Computer science; Medicine; Deep learning; Disease; Radiology; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00230799,0.0006486941,0.0008794655,0.0005762189,0.0003582518,0.0006698894,0.0008568366,0.0008599345,0.0005828217],"category_scores_gemma":[0.00300981,0.000531666,0.001107456,0.0003002594,0.0004373882,0.000627644,0.0007044533,0.001030681,0.0001461528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008670891,"about_ca_system_score_gemma":0.0009288074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008885991,"about_ca_topic_score_gemma":0.007388222,"domain_scores_codex":[0.9994982,0.0001885076,0.00004038761,0.0001423345,0.00007824878,0.00005225045],"domain_scores_gemma":[0.9991085,0.0005580843,0.00004948643,0.00004744626,0.0002038542,0.00003265745],"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":[0.0002598054,0.0001895971,0.00690448,0.00005706967,0.0003564768,0.00009900519,0.0001533452,0.7697603,0.007411849,0.003416648,0.0008491164,0.2105423],"study_design_scores_gemma":[0.0000028785,0.00001968122,0.0003631209,0.000002424542,0.000006976918,0.0000131277,0.000003896168,0.9986664,0.0004830254,0.0003776959,0.00005750213,0.000003232955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1237511,0.0005091737,0.8738577,0.000313311,0.0000563523,0.00009181157,0.00004679546,0.0003301027,0.00104358],"genre_scores_gemma":[0.7465189,0.0002215782,0.2502489,0.0001621147,0.00004741696,0.0001504085,0.0002133559,0.00004144665,0.002395879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008885991,"threshold_uncertainty_score":0.01766849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05049411191802188,"score_gpt":0.3486185636011642,"score_spread":0.2981244516831423,"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."}}