{"id":"W2953096974","doi":"10.1002/hbm.24687","title":"Accurate, rapid and reliable, fully automated MRI brainstem segmentation for application in multiple sclerosis and neurodegenerative diseases","year":2019,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":35,"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; Allergan; Genentech; National Institutes of Health; IXICO; Servier; Schweizerische Multiple Sklerose Gesellschaft; Eisai; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; H. Lundbeck A/S; Baxalta; Universität Basel; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; Bundesministerium für Bildung und Forschung; Northern California Institute for Research and Education; Meso Scale Diagnostics; Teva Pharmaceutical Industries; University of Southern California; Celgene; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Foundation for the National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; Sanofi; Alzheimer's Association; National Science Foundation","keywords":"Brainstem; Atrophy; Segmentation; Multiple sclerosis; Pons; Medicine; Magnetic resonance imaging; Artificial intelligence; Pathology; Radiology; Computer science; Anatomy; Internal medicine","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":[0.001257351,0.0006498861,0.0006081136,0.00102153,0.0003297466,0.0006342566,0.0007813977,0.0008825072,0.0007609167],"category_scores_gemma":[0.003861694,0.0005680916,0.000458163,0.0005789122,0.0003950097,0.0006962012,0.0007971666,0.0003826624,0.0003625199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004839046,"about_ca_system_score_gemma":0.0006188308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002904581,"about_ca_topic_score_gemma":0.005369313,"domain_scores_codex":[0.999274,0.0002333872,0.0000477767,0.0001983232,0.0002043731,0.00004213795],"domain_scores_gemma":[0.9989119,0.0003441038,0.0001918323,0.0002160188,0.0002960993,0.00004000567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00052634,0.00008889687,0.006216256,0.0003151608,0.0002077231,0.0003566321,0.0002552963,0.04531851,0.5483003,0.0008870836,0.001689896,0.395838],"study_design_scores_gemma":[0.00004974226,0.0006839309,0.04582203,0.00009026773,0.0001563921,0.002377803,0.0001238382,0.6752315,0.2667663,0.003815825,0.004699955,0.0001824102],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4559786,0.004268774,0.5300246,0.0003216912,0.00006488313,0.0002114892,0.0006933882,0.006912361,0.001524206],"genre_scores_gemma":[0.6646428,0.0006970589,0.3325095,0.0001125251,0.00003578434,0.0001249044,0.0006145817,0.0003039709,0.0009588677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002904581,"threshold_uncertainty_score":0.006649554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0717417817465214,"score_gpt":0.3159449054704621,"score_spread":0.2442031237239407,"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."}}