{"id":"W3189734184","doi":"10.1097/ju.0000000000002090.11","title":"PD56-11 RAW MICRO-ULTRASOUND TISSUE CHARACTERIZATION USING CONVOLUTION NEURAL NETWORKS TO DIFFERENTIATION BENIGN TISSUE FROM CLINICALLY SIGNIFICANT PROSTATE CANCER","year":2021,"lang":"en","type":"article","venue":"The Journal of Urology","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Prostate cancer; Ultrasound; Convolutional neural network; Prostate; Biopsy; Cancer; Radiology; Prostate biopsy; Artificial intelligence; Internal medicine; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004098916,0.0005121307,0.0002420313,0.0008166727,0.0001703824,0.000585701,0.0004474773,0.0005770791,0.008033662],"category_scores_gemma":[0.001448346,0.0002655048,0.000480706,0.0003366401,0.0001395025,0.0004972476,0.0006113963,0.0004945047,0.002454826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004030454,"about_ca_system_score_gemma":0.0006999216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005182652,"about_ca_topic_score_gemma":0.00849382,"domain_scores_codex":[0.9998313,0.0000168435,0.00001134691,0.00003522846,0.00007679178,0.00002855623],"domain_scores_gemma":[0.9996137,0.00009344947,0.00003559401,0.00004210944,0.0001824572,0.00003265882],"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":[0.001085282,0.0003404891,0.01818458,0.0004139168,0.0001795734,0.0009041677,0.00009397919,0.09535289,0.1057526,0.002617098,0.06183191,0.7132434],"study_design_scores_gemma":[0.00004603512,0.0002868879,0.01823234,0.00007734743,0.00007980719,0.0007330479,0.00005157459,0.8880615,0.06608896,0.001781552,0.02450252,0.00005844028],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3108204,0.002713367,0.6200787,0.00227855,0.001053847,0.0004762155,0.01259281,0.01613222,0.03385384],"genre_scores_gemma":[0.6714098,0.001714579,0.2607224,0.000648908,0.0001749915,0.0004521577,0.02121712,0.000747317,0.0429127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008033662,"threshold_uncertainty_score":0.02687526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02283788418153034,"score_gpt":0.3073510886895595,"score_spread":0.2845132045080291,"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."}}