{"id":"W4313888578","doi":"10.3390/curroncol30010064","title":"Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography","year":2023,"lang":"en","type":"article","venue":"Current Oncology","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Artificial intelligence; Mammography; False positive paradox; Breast cancer; Support vector machine; Population; Gold standard (test); Machine learning; Digital mammography; Medical physics; Cancer; Radiology; Computer science; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0008176005,0.0008073854,0.0005690538,0.002249593,0.0001933744,0.0007251885,0.0006344383,0.0006809468,0.001344842],"category_scores_gemma":[0.00311841,0.0001687966,0.0006666696,0.001467396,0.0002122093,0.0005342517,0.0004821116,0.0006765343,0.0009159842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003242772,"about_ca_system_score_gemma":0.0003337446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001140796,"about_ca_topic_score_gemma":0.0008037921,"domain_scores_codex":[0.9995571,0.0001045421,0.00005556002,0.0001044706,0.0001326742,0.00004569916],"domain_scores_gemma":[0.9987959,0.0006847008,0.000169885,0.00006625211,0.0002532428,0.00003005634],"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":[0.0002515971,0.0003311697,0.01523663,0.0002722944,0.0002007283,0.0002001321,0.0001078891,0.1344963,0.01206362,0.001079293,0.002687458,0.8330728],"study_design_scores_gemma":[0.0000175139,0.0002087809,0.01026024,0.00007227912,0.00008583915,0.0002435225,0.00007255634,0.9755986,0.007772047,0.003318792,0.002319048,0.00003080896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1678865,0.003181409,0.8221478,0.0004579097,0.000181974,0.0002836308,0.0009958467,0.001732038,0.003132936],"genre_scores_gemma":[0.7713071,0.001151647,0.2235312,0.000156016,0.0002251738,0.0003814112,0.001392429,0.00006797256,0.001787072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002249593,"threshold_uncertainty_score":0.004498899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07664421947553403,"score_gpt":0.3359154004739648,"score_spread":0.2592711809984308,"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."}}