{"id":"W2161368485","doi":"10.1109/tbme.2011.2134096","title":"An MRI-Compatible Robotic System With Hybrid Tracking for MRI-Guided Prostate Intervention","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Princess Margaret Cancer Centre; University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Fiducial marker; Imaging phantom; Scanner; Interfacing; Magnetic resonance imaging; Computer science; Tracking (education); Computer vision; Tracking system; Artificial intelligence; Prostate; Interventional magnetic resonance imaging; Biomedical engineering; Medicine; Computer hardware; Kalman filter; Nuclear medicine; Radiology","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":[0.0007616651,0.0005465606,0.0004585784,0.0003839192,0.0002415734,0.0005400039,0.0009807681,0.0005934329,0.002240264],"category_scores_gemma":[0.001326327,0.0002930234,0.0003544411,0.0002446113,0.000296935,0.0006247054,0.000831508,0.0004568133,0.0009151554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001705474,"about_ca_system_score_gemma":0.0004459428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002701513,"about_ca_topic_score_gemma":0.000405732,"domain_scores_codex":[0.9994481,0.0001008136,0.00003400534,0.0001151956,0.0002731962,0.00002872884],"domain_scores_gemma":[0.9995865,0.000105192,0.0001075591,0.00007014775,0.00006186964,0.0000686595],"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":[0.001120643,0.0003074865,0.00196069,0.0007053951,0.0001043838,0.000623657,0.00020403,0.003071617,0.6793305,0.001444887,0.003073444,0.3080533],"study_design_scores_gemma":[0.001208102,0.02305116,0.03216335,0.0001914368,0.000867304,0.02515119,0.0001030003,0.06128575,0.7384638,0.001534415,0.1154706,0.0005098262],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2075943,0.003848908,0.7737445,0.0004131364,0.0004829543,0.001209822,0.0005212984,0.004973043,0.007211967],"genre_scores_gemma":[0.4706033,0.001444333,0.5170234,0.0007211006,0.0002266018,0.000845332,0.0005300237,0.0002309226,0.008375055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002240264,"threshold_uncertainty_score":0.00749445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02028532068096087,"score_gpt":0.2300905414421485,"score_spread":0.2098052207611877,"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."}}