{"id":"W4380987936","doi":"10.3390/s23125648","title":"An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":13,"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; Genentech; IXICO; H. Lundbeck A/S; King Abdulaziz University; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Entorhinal cortex; Deep learning; Hippocampus; Magnetic resonance imaging; Atrophy; Cognition; Artificial neural network; Artificial intelligence; Recall; Dementia; Psychology; Neuroscience; Pattern recognition (psychology); Computer science; Medicine; Disease; Pathology; Cognitive psychology; Radiology","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":[0.0005308776,0.00103489,0.0005226805,0.000504372,0.0002204831,0.0004975605,0.0007630651,0.0008348365,0.0009904512],"category_scores_gemma":[0.0009745256,0.0002900735,0.0006692184,0.0003701905,0.0002095086,0.0003879544,0.000438626,0.0007596351,0.0003030231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008197383,"about_ca_system_score_gemma":0.001438266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02085698,"about_ca_topic_score_gemma":0.01607391,"domain_scores_codex":[0.9998591,0.00002610642,0.00001003777,0.00004199416,0.00002786176,0.00003489343],"domain_scores_gemma":[0.9998233,0.00006557206,0.00001752318,0.00001136079,0.00007155206,0.00001073327],"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":[0.000165555,0.0001164068,0.002313098,0.0000377303,0.00005062078,0.00009260015,0.00002086692,0.9117671,0.003250664,0.0005872637,0.001737079,0.07986114],"study_design_scores_gemma":[0.000004644973,0.00001710456,0.0002494766,0.000003306989,0.000006270855,0.000007578506,0.000002034076,0.9988279,0.0005529289,0.0002359248,0.00009032412,0.000002538012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4118442,0.002091847,0.5753213,0.0009298102,0.0001898345,0.0001901746,0.001453956,0.003067911,0.004910999],"genre_scores_gemma":[0.9290099,0.000398748,0.06477811,0.0002031723,0.00003401994,0.0001871836,0.001484099,0.00005187273,0.003852902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02085698,"threshold_uncertainty_score":0.04147112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04244284395508885,"score_gpt":0.3675219632670021,"score_spread":0.3250791193119132,"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."}}