{"id":"W4400229831","doi":"10.1038/s41597-024-03469-9","title":"HVSMR-2.0: A 3D cardiovascular MR dataset for whole-heart segmentation in congenital heart disease","year":2024,"lang":"en","type":"article","venue":"Scientific Data","topic":"Congenital Heart Disease Studies","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Heart, Lung, and Blood Institute; National Institute on Aging; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Charles H. Hood Foundation; Natural Sciences and Engineering Research Council of Canada; Harvard Catalyst; Philips; National Institutes of Health; U.S. Department of Health and Human Services; National Institute of Neurological Disorders and Stroke; American Heart Association","keywords":"Heart disease; Segmentation; Surgical planning; Magnetic resonance imaging; Medicine; Medical diagnosis; Radiology; Cardiology; Artificial intelligence; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001323835,0.002190795,0.00158762,0.002867657,0.0007606435,0.002169355,0.002951117,0.003221428,0.01138308],"category_scores_gemma":[0.004189916,0.0007957314,0.001749894,0.001748421,0.0005025037,0.0006454918,0.002287984,0.001383486,0.01398753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006621659,"about_ca_system_score_gemma":0.00139947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008275444,"about_ca_topic_score_gemma":0.02422554,"domain_scores_codex":[0.9991502,0.0001413148,0.0001318501,0.0002613683,0.0002260664,0.00008929219],"domain_scores_gemma":[0.9987994,0.0003589524,0.0001156488,0.0003467716,0.0002484405,0.0001306666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001410204,0.0004377692,0.01266115,0.004013407,0.0006366097,0.001653294,0.0004099387,0.009265392,0.01730128,0.001827301,0.8389946,0.1113892],"study_design_scores_gemma":[0.001767904,0.000712553,0.07625075,0.002457257,0.0007664996,0.0145503,0.0006530531,0.05255071,0.02549012,0.01138332,0.8127894,0.0006281557],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04204499,0.00608557,0.03302892,0.00110487,0.0007277336,0.001018013,0.8846532,0.0231794,0.00815738],"genre_scores_gemma":[0.03159311,0.001090091,0.03301206,0.00041008,0.0001621243,0.0008373433,0.9293604,0.001584212,0.001950635],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01138308,"threshold_uncertainty_score":0.03808022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05862689543069959,"score_gpt":0.344718510269828,"score_spread":0.2860916148391284,"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."}}