{"id":"W4415700663","doi":"10.1148/ryct.240568","title":"Artificial Intelligence–based Coronary Plaque Quantification Using Coronary CT Angiography: Current Insights and Future Directions","year":2025,"lang":"en","type":"review","venue":"Radiology Cardiothoracic Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Paul's Hospital; University of British Columbia","funders":"","keywords":"Coronary artery disease; Risk stratification; Coronary angiography; Clinical significance; Vulnerable plaque; Risk assessment; Patient care; CAD","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":[0.001826825,0.0009413281,0.002283827,0.00212728,0.0001861464,0.001809061,0.001087586,0.00136259,0.002609887],"category_scores_gemma":[0.002679049,0.0003605656,0.0009680529,0.00217016,0.0007920223,0.001643595,0.000743305,0.001993242,0.00136873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005966526,"about_ca_system_score_gemma":0.001339222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001198863,"about_ca_topic_score_gemma":0.001579876,"domain_scores_codex":[0.9996527,0.00008149135,0.00006149348,0.00007153985,0.0001086606,0.00002403187],"domain_scores_gemma":[0.9979877,0.001367899,0.0001598288,0.00004833829,0.0003812031,0.00005501254],"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":[0.00007140179,0.00006394168,0.00032688,0.02046988,0.0002073766,0.00007837273,0.00004629877,0.0005404383,0.0005058463,0.003841358,0.01057304,0.9632752],"study_design_scores_gemma":[0.00007262752,0.0003078413,0.004668099,0.02882088,0.00107904,0.001885612,0.0002366856,0.001424141,0.0009310217,0.01637295,0.9440798,0.0001212701],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00007764811,0.9990739,0.0001546779,0.0003180498,0.00007567332,0.000002502234,0.000008437578,0.000005349551,0.0002837827],"genre_scores_gemma":[0.0006935832,0.9986314,0.0002585208,0.0001655625,0.0001321071,0.000004958435,0.00001347904,0.000001401703,0.0000988258],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002609887,"threshold_uncertainty_score":0.009661257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04628394885595958,"score_gpt":0.3761305773458594,"score_spread":0.3298466284898998,"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."}}