{"id":"W4417152952","doi":"10.1093/ehjci/jeaf348","title":"Current and future use of artificial intelligence in valvular heart disease imaging","year":2025,"lang":"en","type":"article","venue":"European Heart Journal - Cardiovascular Imaging","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver General Hospital; University of British Columbia; Artificial Intelligence in Medicine (Canada)","funders":"Takeda Canada; Bristol-Myers Squibb Canada; Butterfly Foundation; Servier; Siemens Healthineers; University of British Columbia; Genome British Columbia; Bayer Fund; Genome Canada; General Electric; Baxter International; Institut de Cardiologie de Montréal; Mayo Clinic; Bayer; National Institutes of Health; AstraZeneca España; Abbott Laboratories; Janssen Biotech; Canadian Institutes of Health Research; National Science Foundation; American Heart Association; National Heart, Lung, and Blood Institute; Pfizer","keywords":"valvular heart disease; Magnetic resonance imaging; Medical imaging; Cardiac magnetic resonance; Intracardiac injection; Cardiac magnetic resonance imaging; Applications of artificial intelligence; Cardiac imaging","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":[0.004902215,0.0006283057,0.0009858523,0.001578027,0.0004031888,0.00360135,0.0012393,0.002733837,0.003540067],"category_scores_gemma":[0.006956057,0.0002849425,0.0007857966,0.001726096,0.002480696,0.004321749,0.001380478,0.003182024,0.001061433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001633103,"about_ca_system_score_gemma":0.002209233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001096293,"about_ca_topic_score_gemma":0.001165442,"domain_scores_codex":[0.9982102,0.0009608397,0.0001433059,0.0002033102,0.0003778366,0.0001045812],"domain_scores_gemma":[0.9904678,0.007908491,0.0002709388,0.0002896486,0.0008080381,0.0002549649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001043617,0.00009750485,0.001056175,0.006857472,0.0001544343,0.0002613773,0.000459475,0.002140835,0.0008305937,0.1047626,0.0214722,0.8618029],"study_design_scores_gemma":[0.00003040604,0.0002512873,0.002416105,0.01152155,0.0001356917,0.0009471257,0.000681593,0.00339071,0.0008411997,0.1562105,0.8234891,0.00008475591],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0007961043,0.9734737,0.004890523,0.01293996,0.000885542,0.00001242716,0.0000279489,0.00004563283,0.006928185],"genre_scores_gemma":[0.01548698,0.9710491,0.006341393,0.003840947,0.002167181,0.00003836691,0.0000508534,0.00002030644,0.001004892],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004902215,"threshold_uncertainty_score":0.0259257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02719133256573007,"score_gpt":0.3345505277069792,"score_spread":0.3073591951412491,"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."}}