{"id":"W4294711935","doi":"10.3389/fcvm.2022.886549","title":"SlicerHeart: An open-source computing platform for cardiac image analysis and modeling","year":2022,"lang":"en","type":"review","venue":"Frontiers in Cardiovascular Medicine","topic":"Anatomy and Medical Technology","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Queen's University","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada; National Heart, Lung, and Blood Institute; Canarie; National Institutes of Health; Children's Hospital of Philadelphia","keywords":"Workflow; Open source; Computer science; Volume rendering; Python (programming language); Image processing; Visualization; Rendering (computer graphics); Computer vision; Medicine; Artificial intelligence; Computer graphics (images); Software; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002725196,0.0005263688,0.005464513,0.00117506,0.0001603283,0.00003267139,0.0007813035,0.0004881578,0.00002928426],"category_scores_gemma":[0.0001516214,0.000473706,0.0012517,0.001495624,0.0001665273,0.0001514362,0.0003598845,0.001049126,7.085047e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003285522,"about_ca_system_score_gemma":0.00006619852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001445869,"about_ca_topic_score_gemma":0.000006064545,"domain_scores_codex":[0.9972307,0.0001603888,0.0008029915,0.0007635432,0.0004679832,0.0005743883],"domain_scores_gemma":[0.9985431,0.000121765,0.0000836929,0.0009939349,0.00002872649,0.000228744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003155429,0.00000771813,0.000006598684,0.003094048,0.009639692,0.00002760922,0.0001630011,0.004381039,2.90456e-8,0.00003156584,0.00110955,0.981536],"study_design_scores_gemma":[0.0005012272,0.00005233112,0.000001042586,0.0007599187,0.01029071,0.00001925654,0.0005446557,0.1986147,9.060085e-8,0.00007362434,0.7887766,0.0003657919],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000004253111,0.6398343,0.357898,0.000007131884,0.0008298744,0.0009112867,0.0000242452,0.0001751805,0.0003157347],"genre_scores_gemma":[0.00004509984,0.9840084,0.01446631,0.00002489085,0.0003960728,0.0002676775,0.0006323947,0.0001369652,0.00002216713],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9811702,"threshold_uncertainty_score":0.9997715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03512966061220395,"score_gpt":0.2971116432484583,"score_spread":0.2619819826362544,"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."}}