{"id":"W4319765238","doi":"10.3390/s23041914","title":"A Convolutional Neural Network and Graph Convolutional Network Based Framework for AD Classification","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Servier; H. Lundbeck A/S; Natural Science Foundation of Beijing Municipality; Eisai; National Natural Science Foundation of China; BioClinica; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health; U.S. Department of Defense","keywords":"Convolutional neural network; Computer science; Graph; Population; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Machine learning; Theoretical computer science; Medicine","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":[0.0005282153,0.001044115,0.000585384,0.001525874,0.0003630243,0.0006250786,0.00126783,0.0007932557,0.001719915],"category_scores_gemma":[0.0009141659,0.0003578557,0.001032145,0.001498602,0.0005099394,0.000880832,0.0006888834,0.00129223,0.000627688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001567844,"about_ca_system_score_gemma":0.001413477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03671039,"about_ca_topic_score_gemma":0.04292178,"domain_scores_codex":[0.9997068,0.00005805524,0.00001378614,0.00009435273,0.00008150206,0.00004543772],"domain_scores_gemma":[0.9998216,0.00004222116,0.00002321317,0.0000279921,0.00006530491,0.00001964525],"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":[0.0001615739,0.000160595,0.002220019,0.0001976084,0.0002225782,0.0002299203,0.00006960698,0.5834681,0.01456823,0.05610926,0.01248728,0.3301052],"study_design_scores_gemma":[0.000005351905,0.00002858903,0.0005881105,0.00001013684,0.00001677367,0.00004390849,0.000005111955,0.9816096,0.001468405,0.01291998,0.003292517,0.0000115241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01067565,0.001193086,0.9823436,0.0003463994,0.00007552416,0.0001076776,0.0008826065,0.001835857,0.002539569],"genre_scores_gemma":[0.3305796,0.0020186,0.6516328,0.000386901,0.0001532462,0.0004229213,0.003948569,0.0002232195,0.01063414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03671039,"threshold_uncertainty_score":0.0729934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0478953458124065,"score_gpt":0.3370199579418627,"score_spread":0.2891246121294562,"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."}}