{"id":"W2925857835","doi":"10.1007/s11548-019-01954-w","title":"Automatic biplane left ventricular ejection fraction estimation with mobile point-of-care ultrasound using multi-task learning and adversarial training","year":2019,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vancouver General Hospital; University of British Columbia; University of British Columbia Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Ejection fraction; Computer science; Artificial intelligence; Segmentation; Computer vision; Biplane; Medicine; Cardiology; Engineering","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":[0.0009202102,0.0008386144,0.0008833939,0.0005805173,0.0002520237,0.0005262197,0.0008780219,0.001205639,0.001166285],"category_scores_gemma":[0.002407712,0.0004622084,0.0006479495,0.000381635,0.0003169336,0.0005511036,0.001256583,0.001404221,0.0007742111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002161727,"about_ca_system_score_gemma":0.000499416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002522242,"about_ca_topic_score_gemma":0.002675309,"domain_scores_codex":[0.9995825,0.0001202598,0.00001782821,0.0001327684,0.00007927242,0.00006736643],"domain_scores_gemma":[0.9992206,0.0004672066,0.00005405411,0.00006601685,0.0001479539,0.00004418513],"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":[0.000778291,0.000428027,0.004379439,0.000171463,0.0001714746,0.0003057639,0.0001140667,0.3389685,0.02599818,0.001711837,0.006110598,0.6208624],"study_design_scores_gemma":[0.000008154838,0.0000475783,0.0007001518,0.000006506007,0.000009956807,0.0000619313,0.000004788919,0.996543,0.001904096,0.0004626567,0.000244741,0.000006419476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03944039,0.0004328839,0.9577496,0.0002157289,0.0001115407,0.00004121759,0.000133511,0.001175826,0.0006992673],"genre_scores_gemma":[0.7829483,0.0003669844,0.2116216,0.0003885654,0.0001881547,0.0001266662,0.000705545,0.0001328292,0.003521382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002522242,"threshold_uncertainty_score":0.005015075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01228849725950498,"score_gpt":0.2606837176183301,"score_spread":0.2483952203588252,"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."}}