{"id":"W3212728405","doi":"10.3389/fcvm.2021.741667","title":"Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps","year":2021,"lang":"en","type":"article","venue":"Frontiers in Cardiovascular Medicine","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Algorithm; Coronary artery disease; Myocardial perfusion imaging; Receiver operating characteristic; Random forest; Boosting (machine learning); Gold standard (test); CAD; Segmentation; Medicine; Computer science; Sensitivity (control systems); Area under curve; Machine learning; Radiology; Cardiology; Internal medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001807984,0.0003901391,0.001742176,0.0004910218,0.0001514881,0.00004494005,0.000144494,0.0001497016,0.00006167025],"category_scores_gemma":[0.006856329,0.0003627236,0.001007894,0.001227933,0.0001579388,0.00008038012,0.0001877265,0.0008995618,0.0000353056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003688652,"about_ca_system_score_gemma":0.0002054462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006201614,"about_ca_topic_score_gemma":0.000008153679,"domain_scores_codex":[0.9962129,0.0003591524,0.0005387337,0.0008714824,0.001357206,0.0006605273],"domain_scores_gemma":[0.9979575,0.0001380717,0.00006154156,0.001016529,0.0003460006,0.0004803479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001811319,0.0001347983,0.7527838,0.0001909498,0.001602535,0.01062843,0.001154404,0.001048383,0.0005113236,0.00003203297,0.05745453,0.1742777],"study_design_scores_gemma":[0.008674826,0.0003922689,0.2775818,0.001413658,0.002343675,0.001926861,0.002159386,0.001305723,0.001028312,0.0001568986,0.7023176,0.000698957],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.02361948,0.7254935,0.1783929,0.008949409,0.01785543,0.001588657,0.0001189447,0.0005644463,0.04341716],"genre_scores_gemma":[0.9294116,0.004969473,0.04486249,0.003909376,0.01115787,0.00009092879,0.001446,0.0002875942,0.003864601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9057922,"threshold_uncertainty_score":0.9998825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00916048603004116,"score_gpt":0.2412256624073948,"score_spread":0.2320651763773537,"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."}}