{"id":"W2903408278","doi":"10.1148/radiol.2018180736","title":"The RSNA Pediatric Bone Age Machine Learning Challenge","year":2018,"lang":"en","type":"article","venue":"Radiology","topic":"Autopsy Techniques and Outcomes","field":"Medicine","cited_by":454,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; University of Toronto","funders":"Leidos; National Cancer Institute; National Institute for Health and Care Research; Radiological Society of North America; Massachusetts General Hospital","keywords":"Medicine; Artificial intelligence; Machine learning; Test set; Convolutional neural network; Bone age; Radiological weapon; Artificial neural network; Set (abstract data type); Upload; Deep learning; Data set; Test (biology); Radiography; Medical physics; Radiology; Computer science; Internal medicine","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":[0.02236002,0.001438393,0.001889861,0.002058327,0.001510239,0.003114031,0.003443959,0.003310199,0.01046993],"category_scores_gemma":[0.0556261,0.0004806029,0.001151352,0.001545984,0.001189047,0.002319601,0.005107884,0.002819282,0.006863356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00275284,"about_ca_system_score_gemma":0.008019812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009295602,"about_ca_topic_score_gemma":0.01620274,"domain_scores_codex":[0.981892,0.006616728,0.001007125,0.002706283,0.006388251,0.001389578],"domain_scores_gemma":[0.9413742,0.02436979,0.002188287,0.004655102,0.0193115,0.008101194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003187517,0.0002956954,0.007165302,0.0006960105,0.0000684569,0.0006248144,0.0003788862,0.006660067,0.002190105,0.002966682,0.7967669,0.1818683],"study_design_scores_gemma":[0.0002201426,0.0005442823,0.02194085,0.0007135023,0.00005505356,0.003170536,0.001332794,0.03987336,0.008274835,0.01333208,0.9103743,0.0001682537],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.2084633,0.02883631,0.2175138,0.2404805,0.04202652,0.003651122,0.1222528,0.02861743,0.1081582],"genre_scores_gemma":[0.3959878,0.007917746,0.3149137,0.02047105,0.01455595,0.005092369,0.1383397,0.006675462,0.09604621],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02236002,"threshold_uncertainty_score":0.1182525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01874844020448894,"score_gpt":0.2910887746923837,"score_spread":0.2723403344878947,"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."}}