{"id":"W1602302494","doi":"10.1118/1.4926428","title":"Image‐based reconstruction of three‐dimensional myocardial infarct geometry for patient‐specific modeling of cardiac electrophysiology","year":2015,"lang":"en","type":"article","venue":"Medical Physics","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Division of Chemical, Bioengineering, Environmental, and Transport Systems; National Center for Chronic Disease Prevention and Health Promotion; National Heart, Lung, and Blood Institute; Johns Hopkins University; American Heart Association; National Institutes of Health; National Science Foundation","keywords":"Myocardial infarction; Medicine; Magnetic resonance imaging; Artificial intelligence; Iterative reconstruction; Computer science; Computer vision; Biomedical engineering; Pattern recognition (psychology); Radiology; Cardiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003095564,0.0001557986,0.0006802706,0.00009172354,0.00003111619,0.000003606863,0.00006650326,0.0001413244,0.00001476724],"category_scores_gemma":[0.001069749,0.0001394993,0.0003869384,0.0002502469,0.0002944769,0.00005118587,0.00004336423,0.0002537211,0.000005874731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006637398,"about_ca_system_score_gemma":0.0006056147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000520135,"about_ca_topic_score_gemma":3.258026e-7,"domain_scores_codex":[0.9982794,0.0000528097,0.0004344474,0.0002487359,0.0007180135,0.0002665795],"domain_scores_gemma":[0.998213,0.0004019296,0.0001419531,0.0002910311,0.0006957569,0.0002563561],"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":[0.007611182,0.003010179,0.06055916,0.001455509,0.002074421,0.00004329248,0.0006344816,0.02027198,0.09896241,0.001492448,0.04723549,0.7566494],"study_design_scores_gemma":[0.02049562,0.007418083,0.008795049,0.001488009,0.002101697,0.0001120359,0.0003696382,0.7076665,0.2075811,0.0389504,0.003510293,0.001511599],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9188866,0.0009661347,0.07740017,0.0003419297,0.001395012,0.0004283752,0.0001017141,0.00004373686,0.0004362967],"genre_scores_gemma":[0.9925576,0.0000232569,0.006073382,0.0002216326,0.0008934746,0.00002339993,0.0001778917,0.0000274778,0.000001814915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7551379,"threshold_uncertainty_score":0.5688615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02243050219613469,"score_gpt":0.2676351227448059,"score_spread":0.2452046205486713,"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."}}