{"id":"W4363650277","doi":"10.1117/12.2653940","title":"Augmenting endometriosis analysis from ultrasound data using deep learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Endometriosis Research and Treatment","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Endometriosis; Pelvic pain; Medicine; Laparotomy; Ultrasound; Laparoscopy; Infertility; Receiver operating characteristic; Stage (stratigraphy); Deep learning; Radiology; Artificial intelligence; Obstetrics; General surgery; Gynecology; Computer science; Internal medicine; Pregnancy","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":[0.001294592,0.0008753893,0.0005769539,0.00143003,0.0001692398,0.0005861914,0.0006008736,0.0006572007,0.000923507],"category_scores_gemma":[0.003795927,0.0002384837,0.0006837398,0.0006462258,0.0002257977,0.0005227988,0.001014198,0.0007883729,0.0004440103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000296305,"about_ca_system_score_gemma":0.0006509164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003361549,"about_ca_topic_score_gemma":0.005185709,"domain_scores_codex":[0.9995854,0.0001498227,0.00003804781,0.00008044588,0.00008143148,0.00006490706],"domain_scores_gemma":[0.99891,0.0006398057,0.00009745052,0.00009850525,0.0002023185,0.00005202955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009092111,0.001017701,0.08088727,0.0004467164,0.0004026789,0.0006518749,0.0001783854,0.2057067,0.02609707,0.0006449218,0.006277577,0.6767799],"study_design_scores_gemma":[0.0000380768,0.0003641497,0.01399971,0.0000718117,0.00007609011,0.000252132,0.00008212008,0.9705382,0.01106963,0.001476349,0.002003693,0.00002804527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7418557,0.004240249,0.2439495,0.001157592,0.0002472232,0.0002659869,0.002899698,0.002421269,0.002962665],"genre_scores_gemma":[0.92636,0.0007581208,0.06704092,0.0002717099,0.00008629732,0.0001266263,0.003979428,0.00003808686,0.001338945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003361549,"threshold_uncertainty_score":0.006846547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.11035772357726,"score_gpt":0.385557659710544,"score_spread":0.275199936133284,"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."}}