{"id":"W4404055048","doi":"10.1016/j.nuclcard.2024.102072","title":"Automatic motion correction for myocardial blood flow estimation improves diagnostic performance for coronary artery disease in 18F-flurpiridaz positron emission tomography-myocardial perfusion imaging","year":2024,"lang":"en","type":"article","venue":"Journal of Nuclear Cardiology","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; National Heart, Lung, and Blood Institute","keywords":"Medicine; Coronary artery disease; Cardiology; Blood flow; Internal medicine; Disease; Radiology; Cardiac PET; Positron emission tomography","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":[0.00328911,0.000616852,0.0008026093,0.0009686167,0.0002541937,0.0008177407,0.0005780003,0.0007133499,0.0006692264],"category_scores_gemma":[0.008249133,0.0003885803,0.0004437664,0.0004562726,0.0003005585,0.0004184128,0.0003952297,0.0004400459,0.0002732951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003092596,"about_ca_system_score_gemma":0.0003979066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001450311,"about_ca_topic_score_gemma":0.00303443,"domain_scores_codex":[0.9982156,0.0008866283,0.0001324767,0.0003211804,0.0003422957,0.00010191],"domain_scores_gemma":[0.9976901,0.001079656,0.0005330193,0.0002392569,0.0003887124,0.00006928052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.009132116,0.0008362704,0.1781949,0.000541873,0.0007210793,0.0004496861,0.0003031279,0.0212427,0.1809005,0.0004791377,0.003498035,0.6037006],"study_design_scores_gemma":[0.0009169402,0.003365197,0.6529114,0.00006863738,0.0006742715,0.002013812,0.00007288594,0.2638037,0.07095164,0.0009567806,0.004057604,0.000207206],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9045336,0.004461465,0.08760906,0.0002024111,0.00007233831,0.0003179931,0.0002696259,0.001240657,0.001292994],"genre_scores_gemma":[0.9438587,0.0005114338,0.05439545,0.0001590576,0.00005957941,0.0001711999,0.0003463296,0.0001020169,0.0003963028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00328911,"threshold_uncertainty_score":0.01739466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00609538796037946,"score_gpt":0.2448671208609748,"score_spread":0.2387717329005953,"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."}}