{"id":"W3158720037","doi":"10.1002/mp.14909","title":"A deep learning‐based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography  images","year":2021,"lang":"en","type":"article","venue":"Medical Physics","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; École de Technologie Supérieure","funders":"","keywords":"Optical coherence tomography; Artificial intelligence; Coronary arteries; Computer science; Segmentation; Coronary atherosclerosis; Vulnerable plaque; Computer vision; Medical imaging; Medicine; Biomedical engineering; Radiology; Artery; Coronary artery disease; Pathology; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102182,0.0001040341,0.0002741574,0.00004001681,0.00003478232,0.000008898268,0.00005352533,0.00008719871,0.0001091389],"category_scores_gemma":[0.0003591009,0.0001015754,0.0001424531,0.0001958622,0.0001533671,0.00006312328,0.0000326057,0.000179597,0.000001253583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002810889,"about_ca_system_score_gemma":0.0002270217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004362227,"about_ca_topic_score_gemma":0.0000118555,"domain_scores_codex":[0.999045,0.00003141267,0.000299449,0.0001800569,0.000280135,0.000163964],"domain_scores_gemma":[0.9993243,0.0001953754,0.0000722932,0.0001358391,0.0001651973,0.000106974],"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":[0.001641563,0.01161972,0.5185065,0.00360385,0.0002676204,0.0005714779,0.001688879,0.02222527,0.2830329,0.002437971,0.00008706579,0.1543172],"study_design_scores_gemma":[0.002269209,0.0004216202,0.1048972,0.001054053,0.0001031425,0.0000192281,0.00006536855,0.8647952,0.0254542,0.0007600313,0.00001785041,0.0001428884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5255394,0.00002901665,0.4740119,0.0001864516,0.00003923026,0.0001462723,0.00001324434,0.00001299714,0.0000214481],"genre_scores_gemma":[0.9966426,0.00002346288,0.002728286,0.0002071498,0.0000643298,0.0000360697,0.0002373308,0.00001727557,0.00004350388],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8425699,"threshold_uncertainty_score":0.4142126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03188894200590674,"score_gpt":0.2928286729677486,"score_spread":0.2609397309618418,"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."}}