{"id":"W2004084279","doi":"10.1118/1.4885959","title":"Evaluating the utility of intraprocedural 3D TRUS image information in guiding registration for displacement compensation during prostate biopsy","year":2014,"lang":"en","type":"article","venue":"Medical Physics","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research","keywords":"Fiducial marker; 3D ultrasound; Image registration; Prostate biopsy; Ultrasound; Computer science; Medicine; Medical imaging; Displacement (psychology); Computer vision; Prostate; Artificial intelligence; Radiology; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001249243,0.0004104506,0.0002825341,0.0007836519,0.0001617213,0.0005990569,0.0003373131,0.0005633738,0.0006968138],"category_scores_gemma":[0.009644486,0.0002229061,0.0003070669,0.0004747904,0.0002594972,0.0005137794,0.0004082906,0.0001992893,0.0002798823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002734937,"about_ca_system_score_gemma":0.0003180026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001276191,"about_ca_topic_score_gemma":0.001883836,"domain_scores_codex":[0.9990989,0.0003782058,0.00007205798,0.0001425275,0.0002636329,0.00004459416],"domain_scores_gemma":[0.997303,0.001501728,0.0003801906,0.0002785083,0.0004562792,0.00008028332],"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":[0.006578175,0.0004863745,0.217246,0.0005352222,0.0003507206,0.0005128963,0.0004610491,0.07759757,0.1976113,0.000447811,0.0004557398,0.4977171],"study_design_scores_gemma":[0.00016162,0.005754645,0.3940453,0.00007095467,0.0004467769,0.003225676,0.000236643,0.3804078,0.213091,0.0003502698,0.002061717,0.0001474842],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9617569,0.001049189,0.03627133,0.00004746261,0.00001391909,0.00005069484,0.0001080194,0.0001644558,0.0005380966],"genre_scores_gemma":[0.9816917,0.0001431736,0.01789863,0.00001477071,0.000007427286,0.00001433532,0.0001092542,0.00002533093,0.00009537501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001276191,"threshold_uncertainty_score":0.006606698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04615145042524999,"score_gpt":0.350093163352675,"score_spread":0.3039417129274251,"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."}}