{"id":"W4210565338","doi":"10.1161/str.53.suppl_1.97","title":"Abstract 97: Development And Validation Of A Deep Machine Learning Tool For Automated Intraventricular Hemorrhage Segmentation And Volume Measurement Using 3d Convolutional Neural Networks","year":2022,"lang":"en","type":"article","venue":"Stroke","topic":"Intracerebral and Subarachnoid Hemorrhage Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Dalhousie University","funders":"","keywords":"Medicine; Convolutional neural network; Segmentation; Intraclass correlation; Intraventricular hemorrhage; Receiver operating characteristic; Artificial intelligence; Intracerebral hemorrhage; Volume (thermodynamics); Deep learning; Pattern recognition (psychology); Computer science; Surgery; Glasgow Coma Scale; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003126258,0.0008794013,0.0004584516,0.001113968,0.0002690512,0.0009306556,0.001282428,0.0007654178,0.003084418],"category_scores_gemma":[0.003942833,0.0004639412,0.0006332844,0.0004977232,0.0003462419,0.0006055327,0.001157319,0.0006026924,0.001669593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009353702,"about_ca_system_score_gemma":0.001466527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005727664,"about_ca_topic_score_gemma":0.004910317,"domain_scores_codex":[0.9991176,0.0001520182,0.00009698898,0.0002063616,0.0003742468,0.00005287703],"domain_scores_gemma":[0.9985268,0.0003590946,0.0001616083,0.0002221135,0.0006645423,0.00006572586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001014882,0.0006378043,0.04786035,0.0004660201,0.0005551479,0.0004783261,0.0001992021,0.1248462,0.143497,0.002237552,0.01161388,0.6665936],"study_design_scores_gemma":[0.00006890049,0.0003928238,0.0201928,0.00007896151,0.0001074961,0.0003814033,0.00003128964,0.8916975,0.08134189,0.0009183236,0.004737422,0.00005119532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3475787,0.0008200841,0.6255465,0.0003514987,0.0001900535,0.0009847858,0.004751862,0.01535656,0.004419972],"genre_scores_gemma":[0.6043039,0.0004857892,0.3757034,0.000263371,0.0000429113,0.001213619,0.01132644,0.0007747624,0.005885844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005727664,"threshold_uncertainty_score":0.01653343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02845251037034276,"score_gpt":0.2714426588826088,"score_spread":0.242990148512266,"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."}}