{"id":"W4387472682","doi":"10.1148/radiol.222441","title":"MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum","year":2023,"lang":"en","type":"article","venue":"Radiology","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Medicine; Neurodegeneration; Biomarker; Alzheimer's disease; Disease; Amyloid (mycology); Pathology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003057474,0.00009230808,0.0001075291,0.00006660272,0.0002882576,0.00003192203,0.00009826748,0.00004010733,0.00004108363],"category_scores_gemma":[0.0002576907,0.00007044006,0.00003892856,0.000362699,0.0002624152,0.00005448811,0.00003466342,0.0001487938,0.00001672835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002043778,"about_ca_system_score_gemma":0.00004480271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001080219,"about_ca_topic_score_gemma":0.0000166034,"domain_scores_codex":[0.9985771,0.0005352094,0.0001776614,0.0003219455,0.0001252492,0.0002628316],"domain_scores_gemma":[0.9992082,0.0003679393,0.0001194097,0.0002096014,0.00001619337,0.00007870629],"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":[0.0001329535,0.00006278595,0.0621557,0.0000183207,0.0000172777,0.00002379308,0.0003008921,0.01307225,0.8994943,0.004783904,0.0005213121,0.01941652],"study_design_scores_gemma":[0.0003188933,0.00007517961,0.6682527,0.000001819035,0.00001603646,0.00001171147,0.00006870434,0.3067656,0.02066079,0.0001356445,0.003612479,0.00008050046],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912927,0.00009209896,0.004012711,0.003725955,0.0003753712,0.0002208671,0.00001794179,0.0001172215,0.0001451007],"genre_scores_gemma":[0.9991651,0.0001098824,0.00003339286,0.0004760404,0.00004351271,0.00004041173,0.00002141191,0.00001343842,0.00009677302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8788335,"threshold_uncertainty_score":0.2872462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0434786283196807,"score_gpt":0.322284145481937,"score_spread":0.2788055171622563,"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."}}