{"id":"W3183730943","doi":"10.1093/ehjci/jeab090.025","title":"Automated myocardial segmentation in native t1-mapping cardiovascular magnetic resonance images based on machine learning: a validation study in the UK biobank\"s covid-19 subset","year":2021,"lang":"en","type":"article","venue":"European Heart Journal - Cardiovascular Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Circle Cardiovascular Imaging","funders":"","keywords":"Magnetic resonance imaging; Segmentation; Biobank; Medicine; Cardiac magnetic resonance; Artificial intelligence; Cardiac magnetic resonance imaging; Coronavirus disease 2019 (COVID-19); Short axis; Cardiac imaging; Cardiology; Nuclear medicine; Internal medicine; Radiology; Computer science; Disease; Mathematics; Bioinformatics; Long axis","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":[0.004184121,0.0007114171,0.0006419405,0.0009638127,0.0005511498,0.0007028098,0.0008457828,0.0009540011,0.001312886],"category_scores_gemma":[0.00839227,0.0002787231,0.0004520314,0.000470819,0.0008764438,0.0003951146,0.001070644,0.0003526515,0.000959319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005638223,"about_ca_system_score_gemma":0.0003496069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006027283,"about_ca_topic_score_gemma":0.005991566,"domain_scores_codex":[0.9981737,0.0009100395,0.0001451884,0.0004240498,0.0002262034,0.0001208502],"domain_scores_gemma":[0.994171,0.002420184,0.0006445947,0.001315335,0.00111353,0.0003352748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02112296,0.003558394,0.7399513,0.0009539811,0.0008405361,0.002448305,0.008115795,0.01220294,0.05857283,0.000431756,0.008404527,0.1433968],"study_design_scores_gemma":[0.0006367781,0.004531791,0.9454083,0.0001150174,0.0002603245,0.00233484,0.00141871,0.03433818,0.007395828,0.0001593437,0.003338309,0.00006261138],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977336,0.0001270561,0.001206675,0.00002726294,0.000008753964,0.0001148651,0.000524251,0.00005162802,0.0002059268],"genre_scores_gemma":[0.992601,0.00009003417,0.003178234,0.0000341312,0.00001814948,0.0001337753,0.003459766,0.00004034099,0.0004445814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006027283,"threshold_uncertainty_score":0.02212805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03266191185165031,"score_gpt":0.3023028033983483,"score_spread":0.269640891546698,"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."}}