{"id":"W3035309915","doi":"10.1002/advs.202000675","title":"Generalizable, Reproducible, and Neuroscientifically Interpretable Imaging Biomarkers for Alzheimer's Disease","year":2020,"lang":"en","type":"article","venue":"Advanced Science","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Key Research and Development Program of China; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; H. Lundbeck A/S; Servier; General Hospital of People’s Liberation Army; National Natural Science Foundation of China; Eisai; Chinese Academy of Sciences; National Institutes of Health; People’s Liberation Army Navy General Hospital; Northern California Institute for Research and Education; University of Pittsburgh; Pfizer; Biogen; BioClinica; Nvidia; F. Hoffmann-La Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Generalizability theory; Neuroimaging; Dementia; Biomarker; Magnetic resonance imaging; Medicine; Artificial intelligence; Cognitive impairment; Imaging biomarker; Disease; Computer science; Machine learning; Psychology; Pathology; Radiology; Psychiatry; Developmental psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005192069,0.0001064516,0.0001285359,0.0001103657,0.0002915983,0.0001335495,0.0001872956,0.000007756148,0.00003072185],"category_scores_gemma":[0.001195111,0.00009132421,0.00004366776,0.0007968689,0.0006775678,0.0005240728,0.0001716808,0.00006695872,0.000007458515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001663832,"about_ca_system_score_gemma":0.0002218913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002613928,"about_ca_topic_score_gemma":2.043966e-7,"domain_scores_codex":[0.9980279,0.00001771818,0.000158316,0.0009292153,0.0004235992,0.0004433001],"domain_scores_gemma":[0.9987446,0.00003705596,0.00003991366,0.0002831727,0.0002456758,0.0006495875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0008998233,0.0001138847,0.0849311,0.0001046175,0.00002314468,0.00006175057,0.000141094,0.00005131201,0.8395226,0.0003751845,0.001236316,0.07253917],"study_design_scores_gemma":[0.005698595,0.001281508,0.3929424,0.0002524215,0.0004295549,0.00005504244,0.000462029,0.265106,0.275141,0.001355085,0.05649615,0.0007802703],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8929255,0.003008405,0.07139182,0.02500964,0.0006023684,0.002696852,0.00004748474,0.0002155213,0.004102385],"genre_scores_gemma":[0.9870023,0.00005663405,0.009047163,0.00346261,0.00004166137,0.0000656007,0.000007073058,0.00001270456,0.0003042488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5643817,"threshold_uncertainty_score":0.3724093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03038177960294311,"score_gpt":0.3375449546296976,"score_spread":0.3071631750267546,"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."}}