{"id":"W4310700692","doi":"10.1002/hbm.26165","title":"Fast three‐dimensional image generation for healthy brain aging using diffeomorphic registration","year":2022,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Center for Innovative Medicine; Eisai Incorporated; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; IXICO; H. Lundbeck A/S; Servier; VINNOVA; Takeda Pharmaceuticals U.S.A.; Barncancerfonden; U.S. Department of Defense; Eli Lilly and Company; China Scholarship Council; Eisai; Fundación CajaCanarias; Alzheimer's Association; Stiftelsen för Gamla Tjänarinnor; Fujirebio US; Pfizer; BioClinica; Biogen; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; AbbVie; Hjärnfonden; F. Hoffmann-La Roche; Merck; Alzheimerfonden; Alzheimer's Drug Discovery Foundation; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; National Center for Advancing Translational Sciences; Demensfonden; Meso Scale Diagnostics","keywords":"Neuroimaging; Artificial intelligence; Image registration; Magnetic resonance imaging; Pattern recognition (psychology); Computer science; Computer vision; Psychology; Image (mathematics); Neuroscience; Medicine; Radiology","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004731286,0.0001459398,0.0001922705,0.000164693,0.001549449,0.00003697142,0.00009336094,0.00002914419,0.00007004826],"category_scores_gemma":[0.00008357823,0.0001703623,0.00008917419,0.0002105023,0.00006109402,0.0001084891,0.00008985962,0.0002612904,0.000001341739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002154783,"about_ca_system_score_gemma":0.00007537321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002966734,"about_ca_topic_score_gemma":0.00001348794,"domain_scores_codex":[0.9986625,0.00005596524,0.000337324,0.0004283425,0.0002525914,0.0002632286],"domain_scores_gemma":[0.9991912,0.0001149662,0.0002026523,0.0003398329,0.00007688247,0.00007448368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001966781,0.00007254313,0.0003382962,0.00006101235,0.000008681998,0.000006134615,0.0001160343,0.0007130835,0.9711605,0.008036186,0.01883566,0.0006321458],"study_design_scores_gemma":[0.004910857,0.001015736,0.01962009,0.0002584225,0.0001053523,0.0008106659,0.0004477678,0.8186009,0.003485667,0.034319,0.1153547,0.001070865],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3617235,0.00005295107,0.6006449,0.03577484,0.00009204719,0.001284676,0.00003287345,0.0002732833,0.0001208504],"genre_scores_gemma":[0.8610122,0.000001226956,0.1228273,0.01310695,0.0009059525,0.0005415487,0.0008234048,0.00007846499,0.0007029627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9676749,"threshold_uncertainty_score":0.9997504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2035637264692,"score_gpt":0.3938933283948056,"score_spread":0.1903296019256056,"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."}}