{"id":"W2736173847","doi":"10.1007/s11548-017-1639-9","title":"Automatic evaluation of vessel diameter variation from 2D X-ray angiography","year":2017,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Segmentation; Coronary arteries; Aorta; Ascending aorta; Artery; Angiography; Medicine; Computer science; Biomedical engineering; Cardiology; Radiology; Artificial intelligence","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":[0.001108289,0.0006142339,0.0009113773,0.003178911,0.00025036,0.0009431434,0.0006444676,0.0008281352,0.0006326347],"category_scores_gemma":[0.002192548,0.0003135133,0.0005243272,0.001133447,0.0001940264,0.0004456078,0.0005899674,0.0003794155,0.0004386553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000166973,"about_ca_system_score_gemma":0.0003467083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009912981,"about_ca_topic_score_gemma":0.001857581,"domain_scores_codex":[0.9991019,0.0001748338,0.00006018964,0.0002525861,0.000320846,0.00008967059],"domain_scores_gemma":[0.9984491,0.0006319102,0.0001861861,0.0001460458,0.0005027989,0.00008404223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001409928,0.0002255225,0.05155066,0.0005422198,0.0003514647,0.0005735019,0.0001648316,0.01165219,0.2930707,0.0005852872,0.00261118,0.6372625],"study_design_scores_gemma":[0.00007505413,0.0004732474,0.2778414,0.00005527272,0.0003737214,0.003798317,0.0001009523,0.6288568,0.08433642,0.0007488427,0.003219221,0.0001208406],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5214861,0.002775809,0.4664432,0.0001451979,0.0001416878,0.0001570116,0.001385263,0.005645633,0.001820029],"genre_scores_gemma":[0.8293202,0.0006419783,0.1672764,0.00006727879,0.0001215964,0.00007091264,0.001339894,0.0003087972,0.0008528874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003178911,"threshold_uncertainty_score":0.005861282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03346239467029961,"score_gpt":0.3193281218018545,"score_spread":0.2858657271315549,"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."}}