{"id":"W2160793620","doi":"","title":"Neural network classification of body surface potential contour map to detect myocardial infarction location","year":2010,"lang":"en","type":"article","venue":"Computing in Cardiology","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Computer science; Myocardial infarction; Body surface; Computer vision; Electrocardiography; Contextual image classification; Cardiology; Medicine; Image (mathematics); Mathematics","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":[0.0006278289,0.0004117961,0.0003435328,0.001032842,0.0001663597,0.0004457491,0.0004664104,0.0005521249,0.002187699],"category_scores_gemma":[0.002635795,0.0001308047,0.0002777461,0.0006137105,0.0001481868,0.0004385442,0.0002472682,0.0004763973,0.0005198209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003782757,"about_ca_system_score_gemma":0.0002331254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004948662,"about_ca_topic_score_gemma":0.004159478,"domain_scores_codex":[0.9997662,0.00006890814,0.00001751503,0.00005237058,0.00006358318,0.00003155626],"domain_scores_gemma":[0.9992028,0.0003975295,0.00004646103,0.00006592678,0.0002596749,0.00002768859],"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":[0.0006420087,0.0003915625,0.01543344,0.00008708519,0.0001140626,0.0001958729,0.0000771596,0.144486,0.01739672,0.001062488,0.005847856,0.8142657],"study_design_scores_gemma":[0.000007785694,0.00003990001,0.003825334,0.000005716772,0.000009286071,0.00002222259,0.000008490665,0.9938877,0.001599185,0.0003741814,0.0002161945,0.000004152591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5185195,0.001255188,0.4698212,0.0007836326,0.0003668583,0.0001823135,0.0007816521,0.002156367,0.006133353],"genre_scores_gemma":[0.9484754,0.0002402902,0.04800186,0.00007952729,0.00006707742,0.00006788162,0.0005080711,0.00003763122,0.002522309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004948662,"threshold_uncertainty_score":0.009839714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01266532221443537,"score_gpt":0.2787828416116795,"score_spread":0.2661175193972442,"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."}}