{"id":"W2319195578","doi":"10.1142/s2424905x16400018","title":"Needle Tracking and Deflection Prediction for Robot-Assisted Needle Insertion Using 2D Ultrasound Images","year":2016,"lang":"en","type":"article","venue":"Journal of Medical Robotics Research","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Health Solutions","keywords":"Deflection (physics); Deflection angle; Ultrasound; Robot; Biomedical engineering; Computer vision; Computer science; Artificial intelligence; Acoustics; Materials science; Optics; Physics; Medicine","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":[0.002065963,0.0001080651,0.000201288,0.000339308,0.0002136414,0.0001128332,0.0001687935,0.0002066219,0.00002553212],"category_scores_gemma":[0.001626114,0.00007929483,0.00007586652,0.0003770637,0.0001347121,0.0002654991,0.00003290613,0.0004712328,0.000002586741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00023702,"about_ca_system_score_gemma":0.0001297881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001434581,"about_ca_topic_score_gemma":0.000008901999,"domain_scores_codex":[0.9978784,0.00007191706,0.000495417,0.0001291606,0.00108151,0.0003435931],"domain_scores_gemma":[0.9977674,0.001156129,0.00008610446,0.0001250212,0.0005482484,0.0003171194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008840716,0.0002382709,0.002988811,0.0002233294,0.0001798396,0.00001666285,0.0002835601,0.102492,0.8032001,0.0008286673,0.004896291,0.08456413],"study_design_scores_gemma":[0.008560932,0.001576244,0.1370973,0.003061006,0.0003531022,0.002998799,0.001749172,0.7716039,0.05622324,0.01162574,0.004257937,0.0008927008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1601871,0.0005027596,0.8366385,0.001898937,0.0004256935,0.000217759,0.000007158254,0.00004438077,0.00007782353],"genre_scores_gemma":[0.9822609,0.001271384,0.01570233,0.0000147995,0.0006602053,0.000009035272,0.000002286827,0.00003583942,0.00004316196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8220739,"threshold_uncertainty_score":0.3233549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1307915798087483,"score_gpt":0.3976418296639164,"score_spread":0.2668502498551681,"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."}}