{"id":"W4393987804","doi":"10.1111/ejn.16332","title":"ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid‐beta from amyloid PET images","year":2024,"lang":"en","type":"article","venue":"European Journal of Neuroscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; U.S. Department of Defense; Eli Lilly and Company; China Scholarship Council; Eisai; Alzheimer's Disease Neuroimaging Initiative; Academy of Finland; Northern California Institute for Research and Education; Helsingin Yliopisto; F. Hoffmann-La Roche; University of Southern California; Pfizer; BioClinica; Biogen; Bristol-Myers Squibb; Meso Scale Diagnostics; Novartis Pharmaceuticals Corporation; Alzheimer's Association","keywords":"Cerebrospinal fluid; Amyloid (mycology); Residual; Amyloid beta; Medicine; Neuroscience; Pathology; Psychology; Computer science; Disease","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":[0.001177575,0.0009809827,0.0006215418,0.0006489711,0.0002061687,0.0006367657,0.001012667,0.0008567419,0.001140826],"category_scores_gemma":[0.001594875,0.0004140478,0.0006014692,0.0003209139,0.0003208411,0.0005511417,0.0006529092,0.0008141765,0.0003465629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001018109,"about_ca_system_score_gemma":0.001012033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01546101,"about_ca_topic_score_gemma":0.01231493,"domain_scores_codex":[0.999797,0.00004651394,0.00001314641,0.00007307057,0.00003464338,0.00003559728],"domain_scores_gemma":[0.9995388,0.0001755348,0.00004842718,0.00004828136,0.000154852,0.00003412148],"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":[0.0008722626,0.0003301058,0.00807104,0.00009734101,0.0002899538,0.0001600137,0.00004593659,0.7946676,0.01238762,0.0008564568,0.002337974,0.1798837],"study_design_scores_gemma":[0.00000750007,0.0000490304,0.0003688852,0.00000338599,0.000009909596,0.000007146435,0.000002624184,0.9982792,0.0009963483,0.0001871659,0.0000850361,0.000003784942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6190026,0.002048716,0.3694296,0.0004951243,0.0001911563,0.0001332553,0.001252484,0.005398521,0.002048533],"genre_scores_gemma":[0.9387498,0.0002994274,0.05572908,0.0001250475,0.00002492261,0.00007219049,0.001264274,0.00005650432,0.003678722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01546101,"threshold_uncertainty_score":0.03074199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03572346916080987,"score_gpt":0.2861022172791315,"score_spread":0.2503787481183217,"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."}}