{"id":"W3196814657","doi":"10.3390/diagnostics11091621","title":"Automated Final Lesion Segmentation in Posterior Circulation Acute Ischemic Stroke Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"Diagnostics","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"National Stroke Foundation; Medical Research Council; National Institute for Health and Care Research; National Health and Medical Research Council; Stryker; Nvidia","keywords":"Deep learning; Convolutional neural network; Segmentation; Artificial intelligence; Transfer of learning; Lesion; Stroke (engine); Computer science; Medicine; Pattern recognition (psychology); Radiology; Surgery","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":[0.000106065,0.0001481962,0.0002204641,0.0001399219,0.00005882465,0.00003184385,0.00004612061,0.0001014318,0.00007765405],"category_scores_gemma":[0.0004932789,0.0001666196,0.00005562165,0.0002812152,0.00002182113,0.0001065744,0.00009812467,0.0002252837,0.00003058827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000290985,"about_ca_system_score_gemma":0.00007340997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001894735,"about_ca_topic_score_gemma":0.000005901883,"domain_scores_codex":[0.9988313,0.00004865348,0.000317051,0.0002746867,0.0002875365,0.000240781],"domain_scores_gemma":[0.9993758,0.0001443684,0.0001182561,0.0001758395,0.000120109,0.00006568024],"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":[0.00006449939,0.0001451603,0.3067095,0.0001173411,0.0001187262,0.001036115,0.0004899601,0.0040403,0.6724291,0.000005262018,0.001488143,0.01335596],"study_design_scores_gemma":[0.004115735,0.0001423407,0.4225677,0.0006335624,0.0007431153,0.0004108003,0.001424505,0.3910334,0.17641,0.000003111649,0.002139155,0.0003766488],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925117,0.0002682385,0.005174726,0.0002438535,0.0001661357,0.0002922271,0.000008492493,0.0001670331,0.001167517],"genre_scores_gemma":[0.9848288,0.0003034977,0.01360866,0.0003347774,0.00009060179,0.00001458106,0.0004938508,0.00003113294,0.00029411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4960191,"threshold_uncertainty_score":0.6794549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03023832691100489,"score_gpt":0.3108858527057659,"score_spread":0.280647525794761,"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."}}