{"id":"W4214895035","doi":"10.3390/app12052627","title":"Cardiac Magnetic Resonance Left Ventricle Segmentation and Function Evaluation Using a Trained Deep-Learning Model","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Compute Canada","keywords":"Ejection fraction; Medicine; Stroke volume; Artificial intelligence; Segmentation; End-diastolic volume; Nuclear medicine; Magnetic resonance imaging; Gold standard (test); Cardiac function curve; Cardiac magnetic resonance imaging; Cardiology; Internal medicine; Computer science; Radiology; Heart failure","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":[0.00138664,0.0008669668,0.0007327296,0.0006916368,0.0002656471,0.0009462413,0.0007772308,0.00122676,0.001102907],"category_scores_gemma":[0.002206599,0.0003305354,0.0006223936,0.0003865001,0.0002871302,0.000468293,0.0005291543,0.0007024318,0.0005857897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001106717,"about_ca_system_score_gemma":0.001251115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007689707,"about_ca_topic_score_gemma":0.009147069,"domain_scores_codex":[0.9997249,0.00004293428,0.00001908889,0.0001219743,0.00005280412,0.00003827681],"domain_scores_gemma":[0.9995396,0.0001434603,0.00004697502,0.00006116644,0.0001803777,0.00002847172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005881408,0.0003255395,0.01297452,0.0001442739,0.0002332725,0.000281991,0.00015656,0.652552,0.05166361,0.001361694,0.003357016,0.2763613],"study_design_scores_gemma":[0.000007820933,0.00005870043,0.001476301,0.000009005076,0.00001542686,0.00003772477,0.00000769226,0.9919071,0.005950894,0.0002855163,0.000235729,0.000008148825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4924878,0.001196258,0.4978848,0.0002707018,0.0001071762,0.0002407376,0.0009916746,0.004234913,0.002585931],"genre_scores_gemma":[0.8397575,0.0002489298,0.1547659,0.0001722781,0.00002627204,0.000218748,0.001584559,0.0002049853,0.003020959],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007689707,"threshold_uncertainty_score":0.0152899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02984751958305828,"score_gpt":0.2935883376933744,"score_spread":0.2637408181103161,"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."}}