{"id":"W4394827550","doi":"10.1016/j.jocmr.2024.100887","title":"Exploration of Geometric Deep Learning FBom 3D Cardiac Shape Models for the Classification of Cardiac Amyloid Sub-type FBom 2D Cine Cardiovascular Magnetic Resonance Imaging","year":2024,"lang":"en","type":"article","venue":"Journal of Cardiovascular Magnetic Resonance","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; The Scarborough Hospital; Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"","keywords":"Medicine; Angiology; Magnetic resonance imaging; Cardiac magnetic resonance; Cardiac magnetic resonance imaging; Amyloid (mycology); Cardiac imaging; Radiology; Cardiology; Artificial intelligence; Internal medicine; Pathology; Computer science","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":[0.0007728486,0.001210196,0.00107827,0.001041436,0.0003277986,0.001065399,0.001272727,0.00176013,0.001313966],"category_scores_gemma":[0.001869773,0.0007237452,0.001596499,0.0006104311,0.000477359,0.0007682479,0.001098589,0.001295038,0.0007118796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005920801,"about_ca_system_score_gemma":0.001121421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009317043,"about_ca_topic_score_gemma":0.01064056,"domain_scores_codex":[0.9997957,0.00004771903,0.000009614182,0.00005854368,0.00004472778,0.00004371517],"domain_scores_gemma":[0.9993531,0.0003635024,0.00006001177,0.00005911739,0.0001116818,0.00005261441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004091323,0.0002341162,0.009278189,0.0001685475,0.0001806693,0.0002328732,0.0001700925,0.7146915,0.01080416,0.003202409,0.005208272,0.25542],"study_design_scores_gemma":[0.000004178527,0.00001906976,0.0002250373,0.000007465118,0.000006311658,0.00003327103,0.00001094267,0.9982887,0.0004544051,0.0007773284,0.0001697496,0.000003481798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2641202,0.001704552,0.7271693,0.001150083,0.00009234007,0.0001087997,0.00114897,0.002644442,0.001861282],"genre_scores_gemma":[0.8303958,0.0007508209,0.1626668,0.0006023142,0.00008570143,0.0001094635,0.002774025,0.0002884845,0.00232669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009317043,"threshold_uncertainty_score":0.0185256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01740171479040785,"score_gpt":0.2227786752581075,"score_spread":0.2053769604676996,"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."}}