{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000994439,0.0006099949,0.0005585827,0.001314998,0.0001724947,0.000466385,0.0005671029,0.0003909093,0.0009305289],"category_scores_gemma":[0.002802193,0.0001823866,0.0004139876,0.0006469061,0.0002201905,0.0004477655,0.0005699791,0.0004659565,0.0003048074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005764342,"about_ca_system_score_gemma":0.0004563348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005220071,"about_ca_topic_score_gemma":0.007734681,"domain_scores_codex":[0.9997076,0.00006896879,0.0000286982,0.0001028099,0.00004034529,0.00005144286],"domain_scores_gemma":[0.9993114,0.0002327766,0.0001559293,0.00006553781,0.0001648508,0.00006947052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001916676,0.001111148,0.5875638,0.0001553751,0.0004463001,0.0005305579,0.0001706909,0.08667226,0.01934665,0.0009956047,0.00484975,0.2962412],"study_design_scores_gemma":[0.00007270036,0.0004893908,0.1654773,0.0000617054,0.0002341431,0.0007477103,0.0001223912,0.8160385,0.01151293,0.003811901,0.001380044,0.00005125852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9600716,0.0006585885,0.03496561,0.0003643761,0.00002334741,0.00007071181,0.002072854,0.0003394756,0.001433488],"genre_scores_gemma":[0.9858595,0.000185252,0.0118596,0.00007145086,0.00001726007,0.00005305445,0.001357461,0.00001146653,0.0005850581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005220071,"threshold_uncertainty_score":0.01037937,"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."}}