{"id":"W3094093963","doi":"10.1016/j.jacc.2020.09.203","title":"TCT CONNECT-190 A Novel Artificial Intelligence Algorithm for Dynamical Coronary Artery Segmentation","year":2020,"lang":"en","type":"article","venue":"Journal of the American College of Cardiology","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre; McGill University","funders":"","keywords":"Medicine; Segmentation; Cardiology; Artificial intelligence; Internal medicine; 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":[0.0004024906,0.0008274257,0.0007223378,0.001397998,0.0006973565,0.001183071,0.001529326,0.001549668,0.005194324],"category_scores_gemma":[0.001219206,0.0004830178,0.0009017908,0.0009683989,0.0004522298,0.0006087991,0.0009215416,0.0009385201,0.001506516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007541875,"about_ca_system_score_gemma":0.001217191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01063927,"about_ca_topic_score_gemma":0.01383797,"domain_scores_codex":[0.9997843,0.00002903783,0.00001343151,0.0000711067,0.00008062134,0.00002157707],"domain_scores_gemma":[0.9996704,0.00012115,0.00002598422,0.00003311889,0.0001239509,0.00002546754],"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":[0.000216617,0.00012068,0.002234711,0.0001153188,0.0001424065,0.0002242814,0.00007799928,0.2377607,0.01675875,0.01066754,0.009213423,0.7224675],"study_design_scores_gemma":[0.00001322844,0.0000271776,0.0002546948,0.000006666232,0.00001185267,0.00007259507,0.000005688081,0.9944872,0.001848131,0.001331854,0.001935073,0.000005792519],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01170206,0.0002437287,0.9819062,0.000172131,0.0001003244,0.00009409205,0.0001298252,0.002683142,0.002968555],"genre_scores_gemma":[0.1574203,0.000269608,0.8348882,0.0002869587,0.0001235535,0.0002596823,0.0006079251,0.000399547,0.005744218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01063927,"threshold_uncertainty_score":0.0211547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02779961644687499,"score_gpt":0.3002305940774911,"score_spread":0.2724309776306161,"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."}}