{"id":"W2990030464","doi":"10.1136/neurintsurg-2019-015471","title":"Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks","year":2019,"lang":"en","type":"article","venue":"Journal of NeuroInterventional Surgery","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"ITEA3; National Institute for Health and Care Research","keywords":"Medicine; Convolutional neural network; Computed tomography; Artificial intelligence; Segmentation; Radiology; Tomography; Computer science","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":[0.001216937,0.0009018157,0.000525256,0.00180507,0.0002165228,0.0007500469,0.0007318939,0.0007401066,0.0006720309],"category_scores_gemma":[0.002794452,0.0004858646,0.0008814709,0.0007019777,0.0003075152,0.0005265506,0.0005800925,0.0004722155,0.0003337662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001055686,"about_ca_system_score_gemma":0.0007622878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01027719,"about_ca_topic_score_gemma":0.01374386,"domain_scores_codex":[0.9995665,0.00009189224,0.00003855075,0.000145718,0.00007580759,0.00008147267],"domain_scores_gemma":[0.9991826,0.0003522481,0.0001975676,0.00008973259,0.0001438749,0.00003389951],"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":[0.002038598,0.0005448435,0.08262413,0.0003034064,0.0009179752,0.0006537423,0.0002454856,0.3255204,0.07072376,0.0009092177,0.004084096,0.5114343],"study_design_scores_gemma":[0.00001921786,0.0001026314,0.02592734,0.00002757245,0.0001087027,0.0001751291,0.00001952451,0.9602664,0.01208668,0.000768324,0.0004776208,0.00002084416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7425459,0.001799253,0.2505073,0.0002963616,0.0000837892,0.0001902551,0.001194922,0.001899917,0.001482236],"genre_scores_gemma":[0.9281187,0.0003976986,0.06894106,0.00009647538,0.00003761291,0.0001025637,0.001517141,0.00006733648,0.0007213832],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01027719,"threshold_uncertainty_score":0.02043474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539619746858281,"score_gpt":0.2512141795818162,"score_spread":0.2358179821132334,"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."}}