{"id":"W4416159454","doi":"10.1007/978-3-032-07904-6_15","title":"Cine-CLIP: Reducing Cine-MRI Dimensionality with Temporal Variability for Left Ventricular Ejection Fraction Estimation","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Ejection fraction; Cardiac magnetic resonance; Fraction (chemistry); Dimensionality reduction; Modality (human–computer interaction); Magnetic resonance imaging; Curse of dimensionality; Biobank; Mean absolute error","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":[0.000846512,0.001253846,0.001198896,0.0007839556,0.0003428179,0.001028166,0.001302066,0.001006822,0.008429167],"category_scores_gemma":[0.002200861,0.0005867341,0.001042514,0.00103905,0.000240829,0.0008936099,0.001224588,0.001267558,0.004433338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002115793,"about_ca_system_score_gemma":0.0006130069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003331846,"about_ca_topic_score_gemma":0.007261293,"domain_scores_codex":[0.9995818,0.0000662461,0.00002014201,0.0001089687,0.0001808821,0.00004197663],"domain_scores_gemma":[0.9992793,0.0003853433,0.0000345774,0.0001109636,0.0001505327,0.00003925995],"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":[0.0003131327,0.0001321586,0.0006425047,0.0001657762,0.0001811379,0.0001450508,0.00007837407,0.0217459,0.0231166,0.002525182,0.04717834,0.9037758],"study_design_scores_gemma":[0.0000537515,0.0001161565,0.002351027,0.00003120246,0.00008990194,0.0003357284,0.00004385312,0.956818,0.01593849,0.006706774,0.0174536,0.0000615119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004339903,0.0008252368,0.9841862,0.0001120653,0.0001967056,0.00006040504,0.001093036,0.008335132,0.0008513758],"genre_scores_gemma":[0.03613507,0.001039361,0.9491825,0.0002140239,0.0003124436,0.0002142269,0.00484163,0.001720156,0.006340586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008429167,"threshold_uncertainty_score":0.02819836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01061874132792136,"score_gpt":0.2740225302509263,"score_spread":0.263403788923005,"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."}}