{"id":"W2801550820","doi":"10.1016/j.compbiomed.2018.05.001","title":"Estimation and tracking of AP-diameter of the inferior vena cava in ultrasound images using a novel active circle algorithm","year":2018,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Hemodynamic Monitoring and Therapy","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Inferior vena cava; Algorithm; Context (archaeology); Computer science; Ellipsoid; Segmentation; Ultrasound; Artificial intelligence; Tracking (education); Computer vision; Medicine; Radiology","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":[0.0009535893,0.0007272402,0.0009143745,0.001426202,0.0005000052,0.0009224645,0.00130617,0.001588346,0.001182002],"category_scores_gemma":[0.00193174,0.0006487181,0.0007105682,0.001138974,0.0004671045,0.0008959323,0.0008609412,0.0006818135,0.0006517514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003514835,"about_ca_system_score_gemma":0.0007526051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002295193,"about_ca_topic_score_gemma":0.002138411,"domain_scores_codex":[0.9993809,0.0001041972,0.00003702716,0.0002209145,0.0002191663,0.00003760997],"domain_scores_gemma":[0.9988029,0.0005076237,0.0001190934,0.0001225069,0.0003915673,0.00005634679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004588019,0.0001742072,0.003641919,0.0002268069,0.0001496076,0.0002133747,0.000292913,0.1097077,0.1035388,0.006091841,0.002585848,0.7729181],"study_design_scores_gemma":[0.00001637725,0.00005670066,0.001343477,0.00000729998,0.00003177335,0.0002154473,0.0000128947,0.9809853,0.01512116,0.0004094199,0.001777934,0.00002213721],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008997757,0.0001670774,0.9899135,0.00003904703,0.00003273353,0.00002603162,0.00001266478,0.000403105,0.0004081858],"genre_scores_gemma":[0.1430579,0.0003089429,0.8539658,0.00005147079,0.00007327428,0.0001263811,0.00009042394,0.0001164511,0.002209322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002295193,"threshold_uncertainty_score":0.005043089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02326717362733625,"score_gpt":0.344973798007236,"score_spread":0.3217066243798997,"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."}}