{"id":"W4389473486","doi":"10.1016/j.bja.2023.11.026","title":"Quantifying ultrasound medical image segmentation for peripheral nerve blocks: a comparison of expert evaluations","year":2023,"lang":"en","type":"letter","venue":"British Journal of Anaesthesia","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"","keywords":"Peripheral nerve; Segmentation; Peripheral; Image segmentation; Ultrasound; Artificial intelligence; Computer vision; Medical ultrasound; Computer science; Image (mathematics); Medicine; Radiology; Anatomy; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01339233,0.0005644748,0.0009973908,0.003203368,0.0004591096,0.002561329,0.0009257809,0.002262233,0.003030433],"category_scores_gemma":[0.04634541,0.0003598437,0.0007059829,0.0007780612,0.0006351901,0.001186545,0.0008926369,0.0006728846,0.001065506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008472654,"about_ca_system_score_gemma":0.0005067915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002458331,"about_ca_topic_score_gemma":0.004527939,"domain_scores_codex":[0.9945853,0.002000017,0.0005929949,0.0005896564,0.002013535,0.0002185677],"domain_scores_gemma":[0.9596046,0.02441675,0.001392819,0.002528967,0.01155233,0.0005045012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005714955,0.0002434865,0.05164078,0.001366906,0.0007212828,0.000802991,0.001308049,0.01142311,0.05693687,0.002397387,0.007562092,0.859882],"study_design_scores_gemma":[0.0004612216,0.0061712,0.4242683,0.001332779,0.002323114,0.02312068,0.002790835,0.3469149,0.1397368,0.008703551,0.04346377,0.0007129417],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6919889,0.01377711,0.2622033,0.004168293,0.001187054,0.0005716911,0.001060202,0.002358059,0.02268543],"genre_scores_gemma":[0.8981045,0.002829979,0.09255586,0.001068988,0.0003090959,0.00007888567,0.0006558383,0.0005717607,0.003824986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01339233,"threshold_uncertainty_score":0.07082623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04530479634838679,"score_gpt":0.3469055263403803,"score_spread":0.3016007299919935,"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."}}