{"id":"W3121033161","doi":"10.1007/s00246-020-02518-5","title":"Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot","year":2021,"lang":"en","type":"article","venue":"Pediatric Cardiology","topic":"Congenital Heart Disease Studies","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Circle Cardiovascular Imaging","funders":"University of Texas Southwestern Medical Center","keywords":"Contouring; Tetralogy of Fallot; Convolutional neural network; Hausdorff distance; Cardiology; Medicine; Internal medicine; Magnetic resonance imaging; Artificial intelligence; Algorithm; Computer science; Radiology; Heart disease","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":[0.001412218,0.001267012,0.0004608795,0.0003968499,0.0002321811,0.0005963751,0.001034732,0.0009188793,0.0004797948],"category_scores_gemma":[0.007008171,0.0003485984,0.0004700425,0.0003086828,0.0004431764,0.0009048842,0.0005067192,0.001737824,0.000415717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000790367,"about_ca_system_score_gemma":0.0005528579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0148087,"about_ca_topic_score_gemma":0.01658673,"domain_scores_codex":[0.9996886,0.00008164294,0.00002225392,0.0001008942,0.00007670873,0.00002984961],"domain_scores_gemma":[0.9986919,0.0005554924,0.00006975037,0.000185208,0.0004084672,0.00008920153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001953709,0.0001875287,0.01104545,0.0001588428,0.0001761182,0.0003366141,0.0002793013,0.2601088,0.01196706,0.001860044,0.008560884,0.7051239],"study_design_scores_gemma":[0.00003395345,0.0003032808,0.007433231,0.0001152085,0.00006328989,0.0002507204,0.0001166808,0.9595065,0.01592765,0.00826483,0.00794173,0.00004302668],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5253626,0.02598471,0.4203573,0.01211534,0.001134305,0.0001870419,0.0004322602,0.003805097,0.01062142],"genre_scores_gemma":[0.8163585,0.004857375,0.1718761,0.0009642174,0.0003576957,0.00006877174,0.0005243054,0.0003165733,0.00467642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0148087,"threshold_uncertainty_score":0.02944499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03006943527286801,"score_gpt":0.2910597795357781,"score_spread":0.2609903442629101,"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."}}