{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008126214,0.0003798069,0.0003524627,0.00019789,0.0004104632,0.0002705685,0.0002516276,0.00005423596,0.00005026431],"category_scores_gemma":[0.00001384826,0.0003173501,0.00006720101,0.0004652089,0.0001377982,0.0003019441,0.000009056717,0.0005011962,0.000003382159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004074661,"about_ca_system_score_gemma":0.00007700596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007974145,"about_ca_topic_score_gemma":0.000009660423,"domain_scores_codex":[0.9975548,0.0003867309,0.000492463,0.0004428378,0.0006235529,0.0004995744],"domain_scores_gemma":[0.9986857,0.0007709411,0.0000967482,0.0002987981,0.00003700194,0.000110868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008888949,0.00007731022,0.000161186,0.00009860816,0.00002250819,0.0005000327,0.00060048,0.9138849,0.08413409,0.00004842099,0.0001728339,0.000210781],"study_design_scores_gemma":[0.001385997,0.0001227021,0.0005417362,0.0001523333,0.00008267766,0.001000006,0.001127024,0.9942035,0.0009244321,0.000003103989,0.00003626087,0.0004202285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8557373,0.00004228339,0.1405592,0.0005312628,0.0003819845,0.000916091,0.0001308342,0.0005534585,0.001147573],"genre_scores_gemma":[0.997041,0.00001057312,0.002193213,0.0003633958,0.000105368,0.000134713,0.00008307332,0.00006141503,0.000007189299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1413038,"threshold_uncertainty_score":0.9999279,"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."}}