{"id":"W4295125836","doi":"10.1016/j.compbiomed.2022.106018","title":"Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization","year":2022,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Queen's University","keywords":"Medicine; Internal medicine; Cardiology; Blood pressure; Coronary artery disease; Ultrasound; Artificial intelligence; Radiology; Machine learning; Computer science","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":[0.0006674547,0.0006763876,0.0009574842,0.0006320929,0.0003366484,0.0006852023,0.001270387,0.0008959742,0.001490487],"category_scores_gemma":[0.0008969628,0.0003235262,0.0007905098,0.0005676316,0.0002473831,0.0005009507,0.000759807,0.001277314,0.000478326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006296037,"about_ca_system_score_gemma":0.001243881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01530819,"about_ca_topic_score_gemma":0.01702736,"domain_scores_codex":[0.9998072,0.00003477104,0.00001388413,0.00006004878,0.00003828076,0.00004597167],"domain_scores_gemma":[0.999729,0.00009401485,0.00002604948,0.00001983928,0.000103165,0.00002779667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003093879,0.0005672986,0.01098583,0.00009751379,0.0002925884,0.0002470309,0.00007261248,0.5221779,0.004914678,0.005882408,0.006391364,0.4480614],"study_design_scores_gemma":[0.000004344,0.00001863133,0.0003025085,0.000004139448,0.00001107907,0.00001219517,0.000002626871,0.9981755,0.0002218713,0.001070812,0.0001735794,0.000002820598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1260297,0.002471312,0.8639246,0.001032259,0.0001737637,0.00008670138,0.0007537324,0.001832493,0.003695398],"genre_scores_gemma":[0.8539549,0.000806182,0.1380524,0.0003651877,0.0001481557,0.0001757736,0.001129477,0.00004291784,0.005325003],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01530819,"threshold_uncertainty_score":0.03043818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02291185243381185,"score_gpt":0.308464145617104,"score_spread":0.2855522931832922,"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."}}