{"id":"W3130628855","doi":"10.1016/j.media.2021.101984","title":"EIS-Net: Segmenting early infarct and scoring ASPECTS simultaneously on non-contrast CT of patients with acute ischemic stroke","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Segmentation; Artificial intelligence; Convolutional neural network; Intraclass correlation; Radiology; Computer science; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006859046,0.0007772904,0.000511597,0.002064302,0.0001544697,0.0007754376,0.0002910488,0.0003446402,0.00271322],"category_scores_gemma":[0.001604825,0.0002006539,0.0002824662,0.0007005889,0.0001059287,0.0006745373,0.0006172516,0.0001765235,0.0006647232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001513611,"about_ca_system_score_gemma":0.0003562015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007808729,"about_ca_topic_score_gemma":0.002111328,"domain_scores_codex":[0.9998259,0.0000459628,0.00003595472,0.00003448093,0.00003362963,0.00002406011],"domain_scores_gemma":[0.9995787,0.0001477977,0.00007263133,0.00002550811,0.00008204817,0.00009323681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01210134,0.0005868867,0.7951528,0.0002445898,0.0005454171,0.000728711,0.00009779201,0.001694679,0.00907027,0.0002058765,0.005967266,0.1736043],"study_design_scores_gemma":[0.0004423531,0.001092955,0.9687378,0.00004551437,0.0005223897,0.00196314,0.0001492558,0.01889095,0.004630468,0.0004224966,0.003053779,0.0000488692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847904,0.0007443664,0.004593956,0.0002147102,0.00005797969,0.0003354413,0.005096974,0.0005269854,0.00363919],"genre_scores_gemma":[0.9771359,0.0005191684,0.01357057,0.00008642852,0.0001742365,0.0002968349,0.006308887,0.00007239191,0.001835602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00271322,"threshold_uncertainty_score":0.009076655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003524322564388348,"score_gpt":0.2279455026463142,"score_spread":0.2244211800819259,"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."}}