{"id":"W4380716250","doi":"10.1161/circ.146.suppl_1.15522","title":"Abstract 15522: Segmentation Improves Deep Learning Accuracy for Differentiating Non-Ischemic and Ischemic Cardiomyopathy Using Cardiac Mri Cine Imaging","year":2022,"lang":"en","type":"article","venue":"Circulation","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Deep learning; Artificial intelligence; Contouring; Magnetic resonance imaging; Cardiomyopathy; Segmentation; Ischemic cardiomyopathy; Cardiac imaging; Radiology; Nuclear medicine; Cardiology; Ejection fraction; Computer science; Heart failure","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00181565,0.0008860264,0.00047489,0.0006784746,0.0002009921,0.0007189274,0.0005095624,0.0008122207,0.001507764],"category_scores_gemma":[0.004059921,0.0002061512,0.000500514,0.0002851439,0.0002705718,0.0005890766,0.000612051,0.0006071927,0.000503131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006373118,"about_ca_system_score_gemma":0.0006917686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003830732,"about_ca_topic_score_gemma":0.005210955,"domain_scores_codex":[0.9995722,0.0001261806,0.00003899454,0.0001255011,0.00007635765,0.00006076319],"domain_scores_gemma":[0.9987826,0.0006136173,0.000109714,0.0001032779,0.0002858449,0.0001050365],"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":[0.003133435,0.001325946,0.1068392,0.0003320886,0.0005342257,0.0002792564,0.0001848487,0.2755973,0.05561328,0.0004560334,0.007615635,0.5480887],"study_design_scores_gemma":[0.00008375635,0.0008175705,0.02178136,0.00003606836,0.00013635,0.0001229321,0.00003176865,0.958554,0.01719282,0.0004766923,0.0007396023,0.00002706506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9544241,0.001086299,0.03976422,0.0005532522,0.0000895484,0.00007075273,0.0004323479,0.001415769,0.002163678],"genre_scores_gemma":[0.9800329,0.0001267509,0.0179601,0.0001623522,0.00002592987,0.00002645769,0.0008282886,0.00004981996,0.0007874765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003830732,"threshold_uncertainty_score":0.009602189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01193334206503988,"score_gpt":0.2753727220599609,"score_spread":0.263439379994921,"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."}}