{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004891882,0.0001610145,0.0005501732,0.0006903053,0.00001695712,0.00001683211,0.0001020365,0.00004165369,0.0004245774],"category_scores_gemma":[0.00006320836,0.000131979,0.0005875885,0.0004191303,0.00007180739,0.0002714025,0.00005433472,0.0002853471,0.000003302609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007396325,"about_ca_system_score_gemma":0.0000808704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001133132,"about_ca_topic_score_gemma":0.000002349117,"domain_scores_codex":[0.9978192,0.000113966,0.001037386,0.0001604146,0.0006615454,0.0002074474],"domain_scores_gemma":[0.9982568,0.000345729,0.0008942811,0.0001332687,0.0002793375,0.00009057358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000933981,0.0004146773,0.9786752,0.0003315297,0.0003819352,0.0002243695,0.00002221729,0.002706805,0.002465917,0.00003538899,0.01227279,0.001535136],"study_design_scores_gemma":[0.003119158,0.0005612863,0.9710702,0.001084175,0.0001124439,0.0008759315,0.00005455184,0.02245733,0.0004775513,0.00001470274,0.00006823675,0.00010438],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963356,0.000222672,0.001721784,0.0005980779,0.0007234192,0.0002514154,0.000008019199,0.00001325008,0.0001257164],"genre_scores_gemma":[0.9983466,0.000007599267,0.00107569,0.000240383,0.000108174,0.000002749148,0.00004017092,0.00001896334,0.0001596304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01975053,"threshold_uncertainty_score":0.5381947,"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."}}