{"id":"W3118658244","doi":"10.1002/mp.14710","title":"Technical Note: Fully automatic segmental relaxometry (FASTR) for cardiac magnetic resonance T1 mapping","year":2021,"lang":"en","type":"article","venue":"Medical Physics","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Health Sciences Centre; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Ontario Ministry of Economic Development, Job Creation and Trade; Government of Canada","keywords":"Steady-state free precession imaging; Medicine; Nuclear medicine; Relaxometry; Cardiac imaging; Cardiac magnetic resonance; Magnetic resonance imaging; Ventricle; Segmentation; Radiology; Artificial intelligence; Cardiology; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00628498,0.0008730345,0.0005972394,0.0008382625,0.0004993837,0.0009333463,0.001776605,0.001169599,0.007597988],"category_scores_gemma":[0.009011571,0.0006609329,0.0007354494,0.0004888354,0.000710646,0.00110391,0.00146788,0.0011409,0.004857355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003847886,"about_ca_system_score_gemma":0.001393987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001797598,"about_ca_topic_score_gemma":0.002903936,"domain_scores_codex":[0.9978369,0.0006294973,0.0001531922,0.0005476252,0.0007395282,0.00009323507],"domain_scores_gemma":[0.9948813,0.0015922,0.0003901199,0.001620206,0.001356028,0.0001602153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001066748,0.0001542306,0.005963838,0.0007320641,0.0001460177,0.001100262,0.0003737122,0.008308297,0.278575,0.008392552,0.03365084,0.6615365],"study_design_scores_gemma":[0.0005021792,0.003004046,0.03783486,0.0003819482,0.000379429,0.04161021,0.0001963927,0.3673817,0.2555276,0.01815995,0.2742755,0.0007463057],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0105294,0.0006275849,0.9831764,0.0006518285,0.0002484663,0.0003293483,0.0004029467,0.002914904,0.001119189],"genre_scores_gemma":[0.04413678,0.0003811767,0.9507285,0.0003838497,0.0002693392,0.0005301348,0.0007932935,0.0006056741,0.002171234],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007597988,"threshold_uncertainty_score":0.03323853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01174487971315904,"score_gpt":0.2764609777736124,"score_spread":0.2647160980604534,"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."}}