{"id":"W2494171310","doi":"10.1007/978-3-319-41501-7_76","title":"Estimating Ejection Fraction and Left Ventricle Volume Using Deep Convolutional Networks","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Ejection fraction; Ventricle; Convolutional neural network; Computer science; Artificial intelligence; DICOM; Diastole; End-systolic volume; Cardiology; Stroke volume; Internal medicine; Computer vision; Medicine; Heart failure; Blood pressure","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":[0.000372376,0.0008718009,0.0005906555,0.0007074117,0.0001151242,0.0007292778,0.0005145521,0.000745484,0.001447165],"category_scores_gemma":[0.001021092,0.0004893496,0.0004916115,0.0004741853,0.0001666285,0.0006592686,0.0005965286,0.0006813426,0.0008618388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003271654,"about_ca_system_score_gemma":0.0003429877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004353951,"about_ca_topic_score_gemma":0.01033366,"domain_scores_codex":[0.9999027,0.00001180153,0.000005104755,0.00004017427,0.00002459202,0.00001566238],"domain_scores_gemma":[0.9998041,0.0001047202,0.00002688778,0.00001804116,0.00003507718,0.00001119295],"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":[0.0003424187,0.0001011413,0.01557463,0.0001342817,0.0001927408,0.0002503101,0.00006718598,0.2256628,0.03346979,0.002662687,0.005654774,0.7158872],"study_design_scores_gemma":[0.00000898544,0.00002679397,0.007893774,0.00003236535,0.00003460777,0.0002078293,0.00001268026,0.9774581,0.007568005,0.005382937,0.00134848,0.00002548281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1007083,0.00343157,0.888826,0.0003815488,0.0001261795,0.00003061146,0.001316988,0.002210448,0.002968438],"genre_scores_gemma":[0.7981679,0.002531694,0.1858132,0.0002322049,0.00023124,0.00005631252,0.002021046,0.000304927,0.0106414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004353951,"threshold_uncertainty_score":0.008657217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0106661184346441,"score_gpt":0.2693799219302406,"score_spread":0.2587138034955965,"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."}}