{"id":"W4407754687","doi":"10.1016/j.jvir.2024.12.238","title":"Abstract No. 192 AI-Assisted Prostate Segmentation Module for MRI-Guided Transurethral Ultrasound (TULSA) Treatment Planning: Multi-reader Multi-case Study and Initial Real-World Experience","year":2025,"lang":"en","type":"article","venue":"Journal of Vascular and Interventional Radiology","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Profound Medical (Canada); Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Medicine; Segmentation; Ultrasound; Medical physics; Multiparametric MRI; Prostate; Radiation treatment planning; Radiology; Artificial intelligence; Internal medicine; Computer science; Radiation therapy","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":[0.0009666889,0.0004630831,0.000319098,0.0004536724,0.0003588026,0.0008101892,0.0007433358,0.000751373,0.03130344],"category_scores_gemma":[0.001897117,0.0004684911,0.0005279198,0.0002233998,0.0002917968,0.0007138474,0.0006353711,0.0006343144,0.01281466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002662615,"about_ca_system_score_gemma":0.0003587451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001223049,"about_ca_topic_score_gemma":0.001622829,"domain_scores_codex":[0.9996679,0.00009096662,0.0000328009,0.00007403826,0.0001081644,0.00002619284],"domain_scores_gemma":[0.9993463,0.0002479721,0.00003349115,0.0001421252,0.0001374188,0.00009257878],"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":[0.003156289,0.001108417,0.02693127,0.0006372333,0.0001928837,0.01339525,0.001128955,0.01244807,0.2430253,0.001073458,0.02257653,0.6743263],"study_design_scores_gemma":[0.0005115107,0.008769607,0.134542,0.0002675812,0.0005338519,0.1697793,0.0009068482,0.1632507,0.3298822,0.001893734,0.1891249,0.0005377153],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.549246,0.002869138,0.3998552,0.001231977,0.000414315,0.0009866095,0.001760866,0.01166515,0.03197084],"genre_scores_gemma":[0.7321991,0.0006912934,0.2219593,0.0006307662,0.0001808246,0.000194352,0.001757821,0.002257752,0.04012865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03130344,"threshold_uncertainty_score":0.1047204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08082336004626445,"score_gpt":0.4229464624490717,"score_spread":0.3421231024028073,"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."}}