{"id":"W4402451222","doi":"10.1016/j.jocmr.2024.101092","title":"Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-dimensional deep learning segmentation model","year":2024,"lang":"en","type":"article","venue":"Journal of Cardiovascular Magnetic Resonance","topic":"Congenital Heart Disease Studies","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University of Toronto","funders":"Departement Economie, Wetenschap en Innovatie; Canadian Institutes of Health Research; Vlaamse regering; Universitaire Ziekenhuizen Leuven, KU Leuven; KU Leuven","keywords":"Medicine; Angiology; Tetralogy of Fallot; Segmentation; Internal medicine; Cardiology; Cardiac imaging; Radiology; Artificial intelligence; Heart disease; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004629103,0.0001417161,0.0005234655,0.0003445487,0.00003877018,0.00001402895,0.00005119483,0.00005518837,0.000007013231],"category_scores_gemma":[0.0001176775,0.0001115653,0.0004241497,0.0005404978,0.00007506092,0.0001408409,0.00002419829,0.0002084056,0.000001617385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000148118,"about_ca_system_score_gemma":0.0001856844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002729494,"about_ca_topic_score_gemma":0.000007144786,"domain_scores_codex":[0.9980373,0.0001145579,0.0005643778,0.0002162938,0.000892206,0.0001752403],"domain_scores_gemma":[0.9990789,0.00004795113,0.00008543709,0.0001915514,0.000526929,0.00006928253],"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":[0.001860108,0.0005081231,0.3145216,0.0009172509,0.002010299,0.001090124,0.0005418657,0.1891326,0.001687589,0.00002150449,0.0000548083,0.4876541],"study_design_scores_gemma":[0.002127268,0.0005430736,0.4117997,0.0006616682,0.0007876645,0.0001222429,0.00003426681,0.5833597,0.0001316585,0.0000195993,0.0003157964,0.00009736347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8487592,0.1486858,0.00208135,0.0000162766,0.00007582067,0.0003290919,0.000002999243,0.00003290963,0.00001661422],"genre_scores_gemma":[0.9926684,0.0004119219,0.006828701,0.000008405224,0.00003199925,0.000005675637,0.000004418567,0.00002450753,0.00001593123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4875568,"threshold_uncertainty_score":0.4549499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01470011460627126,"score_gpt":0.251288913667062,"score_spread":0.2365887990607907,"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."}}