{"id":"W4285743256","doi":"10.1109/lra.2022.3191788","title":"Real-Time Intraoperative Surgical Guidance System in the da Vinci Surgical Robot Based on Transrectal Ultrasound/Photoacoustic Imaging With Photoacoustic Markers: An <i>Ex Vivo</i> Demonstration","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Johns Hopkins University; National Cancer Institute; National Institutes of Health; Intuitive Surgical; National Science Foundation","keywords":"Photoacoustic imaging in biomedicine; Ultrasound; Medicine; Transducer; Endoscope; Medical imaging; Surgical robot; Photoacoustic effect; Surgical instrument; Biomedical engineering; Radiology; Nuclear medicine; Computer science; Artificial intelligence; Robot; Acoustics; Physics; Optics","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":[0.0004446721,0.0005513634,0.0002586959,0.0002396981,0.0001578263,0.0003927257,0.0006362512,0.0005743916,0.001870446],"category_scores_gemma":[0.000380166,0.0002980781,0.0002634025,0.0001131521,0.0002519185,0.0005703657,0.0004062759,0.0004877674,0.0006214242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001606705,"about_ca_system_score_gemma":0.0004572292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005625077,"about_ca_topic_score_gemma":0.0006011815,"domain_scores_codex":[0.9996972,0.00004957938,0.00002071291,0.00006321001,0.0001377289,0.00003147877],"domain_scores_gemma":[0.9997098,0.00004457312,0.00006450119,0.00005858389,0.00007872521,0.00004376906],"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":[0.0002260273,0.0001057103,0.001126894,0.0001675034,0.00002158227,0.000389393,0.0001792356,0.001412801,0.9359458,0.0008163591,0.00157818,0.05803052],"study_design_scores_gemma":[0.0001081687,0.002633372,0.01134468,0.00005065952,0.00007528045,0.007170386,0.000119848,0.04118609,0.9066703,0.0002430486,0.03024883,0.0001492954],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3847847,0.00139356,0.6013777,0.0006116743,0.0003568109,0.0003737696,0.0003072326,0.003727428,0.007066933],"genre_scores_gemma":[0.5883488,0.0007552771,0.4045526,0.0002251878,0.00007291541,0.0002686067,0.0002466345,0.000180327,0.00534965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001870446,"threshold_uncertainty_score":0.006257296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004745358393175111,"score_gpt":0.1986615068723951,"score_spread":0.19391614847922,"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."}}