{"id":"W4414714099","doi":"10.1016/j.cjca.2025.08.105","title":"P187 AUTOMATIC INVERSION TIME SELECTION IN LATE GADOLINIUM-ENHANCED CARDIOVASCULAR MAGNETIC RESONANCE IMAGES USING DEEP LEARNING-BASED SEGMENTATION","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Magnetic resonance imaging; Inversion (geology); Pattern recognition (psychology); Real-time MRI; Image segmentation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0006115055,0.0006870777,0.0005422589,0.001368829,0.0003981729,0.001338201,0.000766283,0.0009563413,0.002424221],"category_scores_gemma":[0.001643495,0.0004174363,0.0005398909,0.000650949,0.0002486861,0.0005848444,0.0005649006,0.0008180423,0.001230749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003830137,"about_ca_system_score_gemma":0.00120928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00506203,"about_ca_topic_score_gemma":0.007241881,"domain_scores_codex":[0.9998431,0.00002728847,0.00001041241,0.00003709498,0.00004493633,0.00003722199],"domain_scores_gemma":[0.9996045,0.0001419902,0.00004298552,0.00003214076,0.0001381904,0.00004014742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001266898,0.0002806618,0.00446106,0.0002992734,0.0001189005,0.0005828851,0.0001375132,0.05644511,0.1498864,0.002930513,0.0114312,0.7721596],"study_design_scores_gemma":[0.00003691389,0.00009329338,0.002728065,0.0000435799,0.00006206286,0.0004901364,0.00003903125,0.9311785,0.05781664,0.003029388,0.004453067,0.00002936973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1039699,0.001284714,0.8854997,0.0005899261,0.0001610783,0.00011248,0.0005333762,0.004646166,0.003202591],"genre_scores_gemma":[0.5169151,0.0009030152,0.4725557,0.0003150073,0.0001324766,0.000088445,0.001445063,0.001262319,0.006382809],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00506203,"threshold_uncertainty_score":0.01006514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008663937508694023,"score_gpt":0.2644753831496129,"score_spread":0.2558114456409188,"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."}}