{"id":"W2159791932","doi":"10.1109/tbme.2010.2048709","title":"Automatic Detection of Lumbar Anatomy in Ultrasound Images of Human Subjects","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Lamina; Ultrasound; Mean squared error; Sonographer; Segmentation; Root mean square; Pearson product-moment correlation coefficient; Computer science; Biomedical engineering; Orientation (vector space); Artificial intelligence; Computer vision; Medicine; Mathematics; Anatomy; Radiology; Physics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000168554,0.0001638933,0.0002603485,0.0004794014,0.00002749391,0.000007353018,0.0001307322,0.0001372726,0.00008264183],"category_scores_gemma":[0.00002638139,0.0001763431,0.00008100602,0.0005144418,0.00009648736,0.0001023553,6.813733e-7,0.0005291491,0.00000290056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004928521,"about_ca_system_score_gemma":0.00001867508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005784471,"about_ca_topic_score_gemma":0.00002943218,"domain_scores_codex":[0.9989746,0.000008208225,0.0003953803,0.0001376998,0.0002283717,0.0002557433],"domain_scores_gemma":[0.9995205,0.0001347718,0.00003280389,0.0001909273,0.0000225591,0.00009846129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001405602,0.00007314243,0.000009661459,0.0002179286,0.00002865856,0.000003969288,0.0001288543,0.02061515,0.9710952,0.000006533037,0.000003707537,0.007815784],"study_design_scores_gemma":[0.0004271314,0.00004431103,0.001912772,0.0001117131,0.00002255298,0.00002372146,0.00003944264,0.1998211,0.7974103,0.00001148712,0.00002307658,0.0001523909],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6505324,0.00002150656,0.3485505,0.000003786984,0.0005575182,0.00008744092,0.00001671366,0.0001705686,0.00005966562],"genre_scores_gemma":[0.9986652,0.0000270742,0.001212922,0.00000272155,0.00002870408,0.00002131431,0.000001974341,0.00003470906,0.000005403424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3481328,"threshold_uncertainty_score":0.7191064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003418036198266196,"score_gpt":0.2104147670607338,"score_spread":0.2069967308624676,"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."}}