{"id":"W4285246255","doi":"10.1109/access.2022.3180073","title":"Automated Detection of Alzheimer’s Disease and Mild Cognitive Impairment Using Whole Brain MRI","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"DoD Alzheimer's Disease Neuroimaging Initiative; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Eisai; National Research Foundation of Korea; National Research Foundation; Ministry of Education; Ministry of Science and ICT, South Korea; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Northern California Institute for Research and Education; F. Hoffmann-La Roche; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Cognitive impairment; Disease; Computer science; Cognition; Medicine; Artificial intelligence; Neuroscience; Psychology; Internal 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":[0.0005141782,0.0008758027,0.0005538039,0.002001219,0.0002245853,0.0006851194,0.0004594861,0.0006689267,0.001231801],"category_scores_gemma":[0.001378552,0.0002061949,0.0005023796,0.0007551508,0.0002327676,0.0006679028,0.0005215963,0.0003544759,0.0007586469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002322595,"about_ca_system_score_gemma":0.0003942961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003874447,"about_ca_topic_score_gemma":0.01058417,"domain_scores_codex":[0.9997882,0.00002918222,0.00001965784,0.00007094361,0.00005398691,0.00003799801],"domain_scores_gemma":[0.9996748,0.00008277893,0.00008067657,0.00004518417,0.00009056293,0.00002596461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005171831,0.0003403061,0.05216258,0.0003874117,0.0002673369,0.0005473974,0.0001246792,0.009410991,0.0896618,0.001217773,0.005907746,0.8394548],"study_design_scores_gemma":[0.00007147838,0.0006064657,0.2893822,0.000265422,0.0005636966,0.005834118,0.0004061318,0.5192497,0.1570574,0.008712093,0.0177124,0.0001388785],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6328199,0.009737912,0.3403527,0.0007821718,0.0002250841,0.000302101,0.003628801,0.003389344,0.008761965],"genre_scores_gemma":[0.8767024,0.002937966,0.1125842,0.0002381259,0.0001619377,0.00009504124,0.002632373,0.00007134203,0.004576663],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003874447,"threshold_uncertainty_score":0.007703781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07835892160765011,"score_gpt":0.3447937614522636,"score_spread":0.2664348398446135,"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."}}