{"id":"W4291415397","doi":"10.3390/brainsci12081067","title":"A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI","year":2022,"lang":"en","type":"article","venue":"Brain Sciences","topic":"Radiomics and Machine Learning in Medical Imaging","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; Servier; Key Laboratory of Nuclear Medicine and Molecular Imaging of Sichuan Province; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Eisai; National Institute on Aging; Alzheimer's Association","keywords":"Alzheimer's Disease Neuroimaging Initiative; Artificial intelligence; Positron emission tomography; Neuroimaging; Cognitive impairment; Radiomics; Support vector machine; Nuclear medicine; Deep learning; Pattern recognition (psychology); Machine learning; Computer science; Psychology; Medicine; Cognition; Neuroscience","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":[0.001864861,0.001352281,0.0009706389,0.001189297,0.0002642174,0.0007177534,0.001386154,0.0009737408,0.0009465032],"category_scores_gemma":[0.002299902,0.0002563054,0.001092184,0.0004243861,0.0003189347,0.000772345,0.0005978784,0.000954246,0.0003721029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001054684,"about_ca_system_score_gemma":0.001132536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008130463,"about_ca_topic_score_gemma":0.006134512,"domain_scores_codex":[0.9994693,0.0001446578,0.00003103039,0.0001793246,0.00008832043,0.00008748156],"domain_scores_gemma":[0.9993864,0.0002332399,0.00006130731,0.00004254616,0.000213575,0.00006280842],"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":[0.001652399,0.001469244,0.05369618,0.0002156418,0.000650265,0.0004522514,0.0001570545,0.5164798,0.01087988,0.001697602,0.007991862,0.4046578],"study_design_scores_gemma":[0.00002503133,0.0001746298,0.001869298,0.00001282685,0.00004848763,0.00005070374,0.00001398236,0.9963003,0.0009343588,0.0003247552,0.0002354515,0.0000101841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7431419,0.002776722,0.2440711,0.001422111,0.0002546377,0.000338394,0.001217323,0.002995303,0.003782554],"genre_scores_gemma":[0.9709858,0.0002927923,0.02516169,0.0003788977,0.0000576766,0.0001558078,0.001371889,0.00003629388,0.001559129],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008130463,"threshold_uncertainty_score":0.01616627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0394434818300856,"score_gpt":0.3239879280370927,"score_spread":0.2845444462070071,"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."}}