{"id":"W4394808105","doi":"10.1016/j.jocmr.2024.100963","title":"Artificial Intelligence-based Classification of ATTR versus AL Cardiac Amyloidosis by Cine-based 3D Shape Phenomics","year":2024,"lang":"en","type":"article","venue":"Journal of Cardiovascular Magnetic Resonance","topic":"Amyloidosis: Diagnosis, Treatment, Outcomes","field":"Biochemistry, Genetics and Molecular Biology","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":"Phenomics; Angiology; Medicine; Cardiac amyloidosis; Amyloidosis; Artificial intelligence; Radiomics; Internal medicine; Radiology; 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":[0.0009973148,0.0004439151,0.0005808339,0.002607232,0.0002108199,0.001673881,0.000397791,0.0009302,0.001191313],"category_scores_gemma":[0.002258218,0.0001620772,0.0007213565,0.0007169883,0.0002597443,0.0003892089,0.0005488987,0.0004423268,0.0005126452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002476989,"about_ca_system_score_gemma":0.0002449324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001735549,"about_ca_topic_score_gemma":0.002265539,"domain_scores_codex":[0.9996622,0.00009127982,0.00003722697,0.00008320798,0.00007854342,0.00004768272],"domain_scores_gemma":[0.9991876,0.0003108534,0.0001425649,0.00006593766,0.0002020232,0.00009096491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00300687,0.0006140786,0.5521877,0.0002146922,0.0004656021,0.001178234,0.0003940322,0.03811974,0.05444793,0.001502621,0.004232308,0.3436361],"study_design_scores_gemma":[0.00005246003,0.0003549933,0.2617753,0.00007152706,0.0002180442,0.001544795,0.0005398968,0.7232081,0.007883303,0.002872172,0.00141434,0.00006500538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9452502,0.000734261,0.04877738,0.000592379,0.00009678524,0.0001003507,0.001735841,0.0003894019,0.002323271],"genre_scores_gemma":[0.9845537,0.000243174,0.01353067,0.00008273213,0.00006363494,0.00003226418,0.001048389,0.00003167929,0.0004137132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002607232,"threshold_uncertainty_score":0.005274415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02424372402329547,"score_gpt":0.269583658360006,"score_spread":0.2453399343367106,"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."}}