{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003502721,0.0001896894,0.000338646,0.0002496375,0.0002855048,0.0000759223,0.00006126246,0.00006231296,0.00001068443],"category_scores_gemma":[0.000368761,0.0001802271,0.00003947318,0.0002425773,0.0001022057,0.0002250972,0.00007815735,0.0001168449,0.00000468419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008809317,"about_ca_system_score_gemma":0.00003205647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005910156,"about_ca_topic_score_gemma":0.00006177763,"domain_scores_codex":[0.998531,0.00009006816,0.0003329673,0.0005278868,0.000207365,0.0003107474],"domain_scores_gemma":[0.9989272,0.0004880823,0.0001171575,0.0002218705,0.0001272099,0.0001185005],"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":[0.000162678,0.00009853049,0.1262804,0.0008056931,0.00006261323,0.000001314256,0.001628173,0.0002517719,0.8598605,0.0001325184,0.003170095,0.007545652],"study_design_scores_gemma":[0.004436138,0.0001831318,0.8666141,0.0002986985,0.00001451555,0.000001593524,0.001053186,0.1257484,0.0009135472,0.00002965905,0.0005442741,0.0001627332],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920287,0.000416248,0.001962388,0.001677898,0.00003594268,0.003464811,0.0000457911,0.0002037377,0.0001645295],"genre_scores_gemma":[0.9963364,0.0002193591,0.001873988,0.0005118761,0.00007054206,0.0006068138,0.0001956697,0.00002623899,0.0001591095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.858947,"threshold_uncertainty_score":0.7349448,"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."}}