{"id":"W4362486628","doi":"10.1117/12.2654393","title":"Using object detection for surgical tool recognition in simulated open inguinal hernia repair surgery","year":2023,"lang":"en","type":"article","venue":"","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Workflow; Computer science; Artificial intelligence; GRASP; Minimum bounding box; Benchmark (surveying); Computer vision; Object detection; Forceps; Visibility; Pattern recognition (psychology); Surgery; Medicine; Database","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":[0.001114299,0.0008044492,0.0006045656,0.001106354,0.0002239587,0.0006809706,0.0007986546,0.0009266165,0.0007874505],"category_scores_gemma":[0.003065911,0.0003607539,0.0006689053,0.0004968825,0.0003482777,0.0003830595,0.0006124679,0.0004579867,0.0003313067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006472369,"about_ca_system_score_gemma":0.0008942987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009865052,"about_ca_topic_score_gemma":0.00943937,"domain_scores_codex":[0.999396,0.00009969873,0.00003459813,0.0001802087,0.0001633741,0.000126183],"domain_scores_gemma":[0.9991311,0.000476623,0.0001010642,0.00007510084,0.0001393115,0.00007670136],"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":[0.001842921,0.0007066104,0.03006242,0.0004031147,0.0002524584,0.001048658,0.0003961118,0.3112808,0.1424486,0.0004143616,0.001581057,0.5095628],"study_design_scores_gemma":[0.00002023487,0.0004416578,0.01688482,0.00002665512,0.00003995984,0.000250206,0.00007762784,0.9467706,0.03463924,0.000284439,0.0005257622,0.00003882004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8052508,0.0006563677,0.1898365,0.0001585019,0.0001308952,0.0001846354,0.0003481585,0.002394713,0.001039306],"genre_scores_gemma":[0.9287843,0.0002152717,0.06952314,0.00005766996,0.00001392661,0.00005410259,0.0004841807,0.000054534,0.0008129015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009865052,"threshold_uncertainty_score":0.01961523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2343982653814993,"score_gpt":0.4064810609783026,"score_spread":0.1720827955968033,"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."}}