{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006647765,0.0001257015,0.0004567374,0.0001006206,0.00005334764,0.0000111817,0.00007577428,0.0001738889,0.000002664284],"category_scores_gemma":[0.0006200492,0.0001338127,0.0001348652,0.0002690673,0.0000880972,0.00002636492,0.00006517157,0.0003982648,0.00001863098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005369612,"about_ca_system_score_gemma":0.00008881455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001001323,"about_ca_topic_score_gemma":0.000012311,"domain_scores_codex":[0.9987645,0.0001652806,0.0003545953,0.0002833787,0.0001425742,0.0002896713],"domain_scores_gemma":[0.9989057,0.0003005844,0.0001095952,0.000324405,0.0002714381,0.00008834525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001461172,0.00001427112,0.6789616,0.00003333429,0.00005937443,0.00001764464,0.00007672412,0.2670521,0.04476822,0.0001067016,0.002661794,0.006102163],"study_design_scores_gemma":[0.0007902711,0.0001262544,0.9615732,0.00003845919,0.00007946225,0.00014282,0.00003225669,0.03577118,0.0002704317,0.00006460182,0.00100066,0.000110379],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.960928,0.0001699581,0.03300906,0.0006897842,0.00427898,0.000339016,0.000002823129,0.00006777482,0.0005146611],"genre_scores_gemma":[0.9951868,0.000004260472,0.002464368,0.0001906503,0.002082715,0.000004490931,0.00003968592,0.00001743079,0.000009573248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2826117,"threshold_uncertainty_score":0.5456722,"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."}}