{"id":"W3134287839","doi":"10.1109/jbhi.2021.3064353","title":"Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data","year":2021,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre; Western University","funders":"Menzies Centre for Australian Studies, King's College London, University of London; Engineering and Physical Sciences Research Council; Agencia Santafesina de Ciencia, Tecnología e Innovación; Université de Rennes 1; Centre For Medical Engineering, King’s College London; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS); National Natural Science Foundation of China; Wellcome Trust","keywords":"Ventricle; Segmentation; Benchmark (surveying); Cardiac magnetic resonance; Computer science; Artificial intelligence; Magnetic resonance imaging; Short axis; Cardiac magnetic resonance imaging; Ground truth; Medicine; Machine learning; Cardiology; Radiology; Mathematics; Cartography; 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":[0.01565531,0.00502293,0.00276606,0.005392863,0.001517628,0.003652707,0.005384366,0.004889388,0.002580672],"category_scores_gemma":[0.02796225,0.0009665489,0.003139053,0.002417648,0.001755327,0.002786278,0.00514427,0.002903601,0.003066793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001787374,"about_ca_system_score_gemma":0.00260522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009960653,"about_ca_topic_score_gemma":0.01414206,"domain_scores_codex":[0.9874303,0.003222983,0.00110974,0.003667295,0.003996124,0.0005734499],"domain_scores_gemma":[0.9883244,0.005282816,0.0007118598,0.002013633,0.00279171,0.000875633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003914149,0.001747018,0.01571497,0.009587336,0.003681619,0.001285266,0.0009558109,0.1110085,0.05317304,0.0046259,0.2608612,0.5334452],"study_design_scores_gemma":[0.00146254,0.003422827,0.04425362,0.002260235,0.001182858,0.006466175,0.001086898,0.7263888,0.06823497,0.01283191,0.1318067,0.0006025865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3381051,0.07875824,0.3482846,0.007880207,0.007747369,0.003396783,0.09117153,0.1074911,0.01716509],"genre_scores_gemma":[0.3032591,0.008711714,0.3796751,0.003510654,0.001175317,0.001354812,0.2832801,0.01014816,0.008885042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01565531,"threshold_uncertainty_score":0.08279419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1869169490336924,"score_gpt":0.4841103450907501,"score_spread":0.2971933960570576,"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."}}