{"id":"W4391972224","doi":"10.1093/ehjci/jeae045","title":"Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry","year":2024,"lang":"en","type":"article","venue":"European Heart Journal - Cardiovascular Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Center for Advancing Translational Sciences; National Heart, Lung, and Blood Institute; National Institutes of Health; Narodowa Agencja Wymiany Akademickiej; British Heart Foundation","keywords":"Calcification; Medicine; Artery; Internal medicine; Cardiology; Artificial intelligence; Radiology; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002192707,0.0002866531,0.0004957302,0.0004262167,0.0002620415,0.0004624244,0.000109782,0.00004096926,0.00002056968],"category_scores_gemma":[0.0002554134,0.0002584841,0.000652995,0.0005759929,0.00008523096,0.000343374,0.00004666942,0.001220568,0.0003271061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002512762,"about_ca_system_score_gemma":0.0001294238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009209955,"about_ca_topic_score_gemma":9.51243e-7,"domain_scores_codex":[0.9969282,0.0008096452,0.0005315893,0.0005711514,0.0006325998,0.0005268074],"domain_scores_gemma":[0.9986758,0.0001345217,0.00008544268,0.0006108457,0.0002475059,0.0002459034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003201133,0.0006956281,0.7503186,0.0007472104,0.002428978,0.08224482,0.005433863,0.01802526,0.009070656,0.00006770972,0.02359418,0.1070529],"study_design_scores_gemma":[0.002648712,0.00003611732,0.6931899,0.001923238,0.0004660399,0.03777828,0.001608824,0.05847435,0.0001220469,0.000001983241,0.2033598,0.000390687],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4986764,0.2591224,0.2244204,0.0056818,0.002737866,0.001283701,0.00002275361,0.003445398,0.004609246],"genre_scores_gemma":[0.9949958,0.001119662,0.002648721,0.0001392885,0.0006395585,0.000004952705,0.0001016376,0.000144004,0.0002064228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4963193,"threshold_uncertainty_score":0.9999868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399992594351583,"score_gpt":0.2773047354749889,"score_spread":0.2533048095314731,"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."}}