{"id":"W4200627902","doi":"10.1002/cnm.3559","title":"Automating fractional flow reserve (FFR) calculation from CT scans: A rapid workflow using unsupervised learning and computational fluid dynamics","year":2021,"lang":"en","type":"article","venue":"International Journal for Numerical Methods in Biomedical Engineering","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Medical Research Council Canada; Swansea University; Global Challenges Research Fund","keywords":"Fractional flow reserve; Computational fluid dynamics; Computer science; Workflow; Atheroma; Artificial intelligence; Radiology; Medicine; Coronary angiography; Cardiology; Engineering; Myocardial infarction","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001337889,0.001107204,0.000989238,0.00167,0.0005674576,0.001375727,0.00143703,0.001087494,0.003198533],"category_scores_gemma":[0.005232253,0.0008346789,0.000948581,0.0007761905,0.0005209998,0.0008684674,0.001639412,0.001595579,0.002969077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004187823,"about_ca_system_score_gemma":0.002104886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00270777,"about_ca_topic_score_gemma":0.004756394,"domain_scores_codex":[0.9991365,0.0001560369,0.00007825754,0.0002410281,0.0003410926,0.00004713046],"domain_scores_gemma":[0.9976434,0.001180718,0.0002345532,0.000372768,0.0004667085,0.0001018228],"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":[0.0003188243,0.0003259426,0.006510183,0.000545972,0.0001755668,0.0003867115,0.0003014669,0.0552527,0.1275271,0.003812897,0.01338554,0.7914571],"study_design_scores_gemma":[0.00007927464,0.0001395302,0.007957897,0.00007766945,0.00006982039,0.001134856,0.00009598803,0.8917817,0.06828368,0.01294392,0.01727135,0.0001643964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005985812,0.0001560824,0.9883323,0.0001611201,0.00003866866,0.0001393142,0.0003385328,0.004364006,0.0004841641],"genre_scores_gemma":[0.05145743,0.0002941534,0.9449197,0.0001352168,0.00007201235,0.0003452073,0.0008056971,0.0009150438,0.0010554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003198533,"threshold_uncertainty_score":0.01070017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03134073442057932,"score_gpt":0.3987160302871332,"score_spread":0.3673752958665539,"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."}}