{"id":"W4296079510","doi":"10.21203/rs.3.rs-1995557/v1","title":"Development of an Image Classification Pipeline for Atherosclerotic Plaques Assessment using Supervised Machine Learning","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Atherosclerosis and Cardiovascular Diseases","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Pipeline (software); Fatty streak; Preprocessor; Computer science; Feature extraction; Vulnerable plaque; Pattern recognition (psychology); Pathology; Medicine; Lesion","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":[0.00223965,0.002028231,0.001311392,0.002988789,0.0007988902,0.001660577,0.002467817,0.00160008,0.008225993],"category_scores_gemma":[0.003476805,0.0008942658,0.001805964,0.00117092,0.0004366444,0.001388125,0.00132562,0.001842529,0.007048908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165237,"about_ca_system_score_gemma":0.0018905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004513551,"about_ca_topic_score_gemma":0.004215233,"domain_scores_codex":[0.998549,0.0001767614,0.0001312286,0.0004952162,0.0004788527,0.0001689206],"domain_scores_gemma":[0.9977769,0.0004739532,0.0001849356,0.0002723123,0.001176166,0.0001156757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004380673,0.0006662405,0.004413044,0.0003439307,0.00020465,0.0002796957,0.0001700308,0.03145725,0.0713159,0.002291661,0.01819297,0.8702266],"study_design_scores_gemma":[0.00003322347,0.0001363317,0.001833231,0.00002730176,0.00003202391,0.00008493413,0.00003598266,0.929592,0.05921847,0.002914964,0.006052596,0.00003911345],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01075273,0.0001410802,0.9459546,0.0001629535,0.00007376057,0.0004504395,0.0008032752,0.04063682,0.001024429],"genre_scores_gemma":[0.07766256,0.0001266278,0.9140298,0.0001595144,0.00004532376,0.0008180984,0.002932215,0.0006663391,0.003559423],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008225993,"threshold_uncertainty_score":0.02751875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1409015137061411,"score_gpt":0.410905030161334,"score_spread":0.2700035164551929,"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."}}